综合_计算机基础理论_计算机类_最新资料_得益网

《电脑常见故障处理》视频教程

2008-08-26 21:57:52

新电脑课堂－－《电脑常见故障处理》，分3部分，第一部分计算机硬件基础(掌握处理故障时，必须的硬件知识，动漫人物伴学)，第二部分详解故障实例(实战演练，手把手的教，同样是动漫人物伴学)，第三部分电子书一套。（普及国人计算机实用水平，低价10分放送）

超级容易学电脑——电脑故障恢复简易行

2008-08-26 21:01:11

用电脑时，谁能保证不发生电脑故障呢？有了故障怎么办？拿去维修吗？其实很多时候都没有那个必要。有了本教学光盘，电脑故障恢复没有你想象的那么难，自己DIY就OK！在长达4个小时的超清晰多媒体教学视频中，详细阐述了电脑故障概述，死机故障处理，BIOS与主板故障原理与解决，CPU、内存、硬盘、显卡、声卡、软驱、光驱、外围设备、电源等故障原理与相应解决方案。由0基础晋升到各大计算机维修中心专业工作人员的水平！（普及国人计算机维修DIY，本视频资料超低价5分送给大家！觉得好的话，顶一下，大家赚点积分也不容易，很多人一天才加一分，更多计算机实用精彩视频超低价甚至免费放送...敬请期待>>.）

Wavelets in Medical Image Processing Denoising,Segmentation,and Registration

2008-08-09 11:15:26

Wavelet transforms and other multi-scale analysis functions have been used for compact signal and image representations in de-noising, compression and feature detection processing problems for about twenty years. Numerous research works have proven that space-frequency and space-scale expansions with this family of analysis functions provided a very efficient framework for signal or image data.
The wavelet transform itself offers great design flexibility. Basis selection, spatial-frequency tiling, and various wavelet threshold strategies can be optimized for best adaptation to a processing application, data characteristics and feature of interest. Fast implementation of wavelet transforms using a filter-bank framework enable real time processing capability. Instead of trying to replace standard image processing techniques, wavelet transforms offer an efficient representation of the signal, finely tuned to its intrinsic properties. By combining such representations with simple processing techniques in the transform domain, multi-scale analysis can accomplish remarkable performance and efficiency for many image processing problems. 1
Multi-scale analysis has been found particularly successful for image de-noising and enhancement problems given that a suitable separation of signal and noise can be achieved in the transform domain (i.e. after projection of an observation signal) based on their distinct localization and distribution in the spatial-frequency domain. With better correlation of significant features, wavelets were also proven to be very useful for detection {jin_Mallat_1992a} and matching applications {jin_Strickland_1995}.
One of the most important features of wavelet transforms is their multi-resolution representation. Physiological analogies have suggested that wavelet transforms are similar to low level visual perception. From texture recognition, segmentation to image registration, such multi-resolution analysis gives the possibility of investigating a particular problem at various spatial-frequency (scales). In many cases, a “coarse to fine” procedure can be implemented to improve the computational efficiency and robustness to data variations and noise.
Without trying to cover all the issues and research aspects of wavelet in medical imaging, we focus our discussion in this chapter to three topics: image de-noising/enhancement, image segmentation and image registration using wavelet transforms. We will introduce the wavelet multi-scale analysis framework and summarize related research work in this area and describe recent state-of-the-art techniques.

计算机系统设计与结构（第2版）

2008-08-06 20:49:29

【内容简介】
　　本书从系统结构设计师、汇编程序员和逻辑设计师的角度介绍了计算机系统结构的设计。全书从计算机系统结构设计的综述入手，讲解了机器和机器语言之间的关系，引入了有代表性且容易理解的SRC模型和RTN结构功能描述语言，并讨论了相关的逻辑设计问题；接下来作者用实例说明了CISC和RISC的区别，深入剖析了指令集和硬件之间的接口关系，介绍了CPU流水线、多指令发射计算机、微代码控制单元的设计以及算术逻辑处理单元的设计；之后作者详细介绍了存储器的层次化结构设计，并且讨论了机器输入输出系统和外围设备；最后作者讨论了一些计算机网络互连方面的论题。
　　本书可作为高校计算机、电子等相关专业本科生和研究生微机原理、系统结构和计算机设计等方面谭程的教材，对相关专业人士和研发人员也很有裨益。
【作者简介】
　　Vincent P.Heuring，美国科罗拉多大学博尔德分校电子和计算机工程系教授，研究方向包括计算机系统结构和编程语言的设计与实现，主要关注计算机硬件和软件的关系。
　　Heuring教授是Eli编译器构建系统的主要设计者之一，他还和Jordan教授合作设计了世界上第一台光学存储程序计算机。
【下载说明】
　　本资料为《计算机系统设计与结构（第2版）》一书PDF格式的清晰电子版，推荐使用Adobe Reader 7.0或兼容阅读工具打开！
【图书目录】
第1章通用计算机
1.1 通用计算机
1.2 用户眼中的计算机
1.3 机器语言与汇编语言程序员眼中的计算机
1.4 计算机架构师眼中的计算机
1.5 逻辑设计师眼中的计算机
1.6 历史回顾
1.7 研究现状与发展趋势
1.8 本书的讲解方式
小结
参考文献
习题
第2章机器，机器语言和数字逻辑
2.1 计算机及机器指令的分类
2.2 计算机指令集
2.3 简化RISC计算机的非形式化描述
2.4 使用寄存器转移标记语言对SRC机进行形式描述
2.5 使用RTN语言对寻址模式进行描述
2.6 寄存器转移与逻辑电路：从行为到硬件
小结
参考文献
习题
第3章几种真实机器
3.1 计算机功能与性能
3.2 精简指令集计算机与复杂指令集计算机的比较
3.3 CISC处理器：摩托罗拉MC68000
3.4 一种RISC计算机体系结构：SPARC机
小结
参考文献
习题
第4章处理器设计
4.1 处理器设计过程
4.2 1总线SRC计算机的微观结构
4.3 数据通道实现
4.4 2总线SRC机的逻辑设计
4.5 计算机控制单元
4.6 2总线和3总线处理器设计
4.7 机器复位
4.8 机器异常
小结
参考文献
习题
第5 章处理器设计高级议题
5.1 流水线结构
5.2 流水线冲突
5.3 指令级并行
5.4 微编码
小结
参考文献
习题
第6章计算机算法与算术单元
6.1 数字系统与基数转换
6.2 定点算术
6.3 算术单元ALU设计的半数值情形
6.4 浮点算术
小结
参考文献
习题
第7章内存系统设计
7.1 导言：内存系统的组成单元
7.2 RAM结构：逻辑设计师的视角
7.3 内存电路板和模块
7.4 双层内存架构
7.5 Cache
7.6 虚拟内存
7.7 计算机的存储子系统
小结
参考文献
习题
第8章输入和输出
8.1 I/O子系统
8.2 可编程I/O
8.3 I/O中断
8.4 直接内存访问
8.5 I/O数据格式转换和错误控制
小结
参考文献
习题
第9章外部设备
9.1 磁盘驱动器
9.2 改善磁盘系统的性能和可靠性
9.3 其他海量存储设备
9.4 视频显示设备
9.5 打印机
9.6 输入设备
9.7 与模拟世界之间的接口
小结
参考文献
习题
第10章通信，网络和Internet
10.1 计算机与计算机之间的数据通信
10.2 串行数据通信协议
10.3 局域网
10.4 现代串行总线：USB和火线
10.5 Internet
小结
参考文献
习题
附录A SRC机的RTN描述
附录B 汇编与汇编器
附录C 部分习题及解答
附录D 简单RISC计算机
附录E SRC汇编语言约
索引

Advances in Nonlinear Signal and Image Processing (超清晰版)

2008-07-28 15:53:28

1. Nonstationary stochastic differential equations,
Lorenzo Galleani and Leon Cohen 1
1.1. Introduction 1
1.2. Time-dependent power spectrum 2
1.3. The equation of motion for a nonstationary stochastic system 3
1.4. The nonstationaryWiener process 5
1.5. The Langevin equation: the full exact solution 7
1.6. Quantum Langevin equation 9
1.7. Time-variant random systems 10
1.8. Summary 13
Bibliography 13
2. Aperture filters: theory, application, and multiresolution analysis,
Roberto Hirata Jr., Marcel Brun, Junior Barrera,
and Edward R. Dougherty 15
2.1. Introduction 15
2.2. Window operators 17
2.3. Aperture operators 18
2.4. Envelope aperture 25
2.5. Multiresolution aperture 33
2.6. Summary 41
Bibliography 45
3. Finite-set signal processing, Ronald K. Pearson and Moncef Gabbouj 49
3.1. Introduction 49
3.2. Fundamental notions 51
3.3. Characterization on finite sets 56
3.4. Filters on finite sets 68
3.5. Variations and extensions 73
3.6. Summary 74
Bibliography 75
4. Nonlinear signal modeling and structure selection with applications
to genomics, Ioan Tabus, Jorma Rissanen, and Jaakko Astola 79
4.1. Introduction 79
4.2. Preliminaries: modeling and predicting gene expressions 80
4.3. Several classes of nonlinear functions and associated
design methods 83
4.4. Normalized maximum likelihood models for a class of
Boolean regressor models 92
4.5. Summary 100
Bibliography 100
5. Nonlinear methods for speech analysis and synthesis,
Steve McLaughlin and Petros Maragos 103
5.1. Introduction 103
5.2. What nonlinear methods might we use? 108
5.3. Summary 136
Bibliography 136
6. Communication system nonlinearities: challenges and some
solutions, G. Tong Zhou, Hua Qian, and Ning Chen 141
6.1. Introduction 141
6.2. Nonlinear communication system concepts 142
6.3. Nonlinear distortions 150
6.4. Digital baseband predistortion linearization 156
6.5. Summary 164
Bibliography 165
7. Nonlinear multichannel active noise control,
Giovanni L. Sicuranza and Alberto Carini 169
7.1. Introduction 169
7.2. The active noise control scenario 171
7.3. Nonlinear active noise controllers 184
7.4. A class of nonlinear feedforward active noise controllers 187
7.5. Simulation results 195
7.6. Current work and future developments 198
7.7. Summary 200
Bibliography 200
8. Chaotic sequences for digital watermarking,
Nikos Nikolaidis, Anastasios Tefas, and Ioannis Pitas 205
8.1. Introduction 205
8.2. Correlation-based watermarking schemes employing
Markov chaotic sequences 207
8.3. Watermark generation by chaotic mixing 229
8.4. Other applications of chaotic systems in digital watermarking 235
8.5. Summary 235
Bibliography 236
9. Modeling of evolving textures using granulometries,
A. J. Gray, S. Marshall, and J. McKenzie 239
9.1. Introduction 239
9.2. Textures and texture analysis 239
9.3. Granulometries 245
9.4. Parallel evolution functions 249
9.5. Application to corrosion images 252
9.6. Modeling the texture 254
9.7. Summary 263
Bibliography 266
10. Multichannel weighted medians, Yinbo Li and Gonzalo R. Arce 273
10.1. Introduction 273
10.2. Multichannel weighted median filtering structures 275
10.3. Filter optimization 279
10.4. Complex multichannelWMs and their optimization 285
10.5. Simulations 290
10.6. Summary 297
Bibliography 298
11. Color image processing: problems, progress, and perspectives,
E. R. Davies and D. Charles 301
11.1. Introduction 301
11.2. The color problem 303
11.3. Linear versus nonlinear processing 303
11.4. Color filtering 305
11.5. Color bleeding 306
11.6. The mode filter 310
11.7. Modern “switched” noise suppression filters 313
11.8. Filters with adjustable parameters 315
11.9. Distortions produced by median and other filters 316
11.10. Review of other color work 322
11.11. Summary 325
Bibliography 326
12. Nonlinear edge detection in color images, Adrian N. Evans 329
12.1. Introduction 329
12.2. Color edge detection 330
12.3. Color spaces and distance measures 331
12.4. Color edge detectors based on vector differences 333
12.5. Vector order statistics color edge detectors 337
12.6. Color morphological gradient operators 340
12.7. Results and evaluation 343
12.8. Summary 351
Bibliography 353
Index 357

电脑维护视频教程(非常实用的课程)

2008-07-24 12:42:30

简介：曙光与咖啡豆一个扮演老师，一个扮演学生，交互式互动学习电脑维护的方方面面。
视频目录：
第一章：电脑硬件介绍
第一课：电脑的硬件组成
第二课：CPU
1、 CPU的性能指标
2、 CPU的接口类型及选购原则
3、主流CPU的推荐
第三课：主板
1、主板的元件组成
2、主板的芯片组及主板选购要点
3、主流主板的推荐
第四课：硬盘
1、硬盘介绍及选购要点
2、主流硬盘推荐
第五课：内存
1、内存介绍及选购要点
2、主流内存的推荐
第六课：光驱
1、光驱的介绍及选购要点
2、主流光驱的推荐
第七课：显卡
1、显卡的介绍及选购
2、主流显卡的推荐
第八课：显示器
1、 CRT与LCD显示器的介绍及选购要点
2、主流CRT与LCD显示器的推荐
第九课：声卡与网卡
1、声卡与网卡的功能及分类
第十课：键盘和鼠标
1、键盘和鼠标的分类及选购要点
第十一课：机箱和电源
1、机箱和电源的介绍及选购要点
第二章：电脑组装过程
第十二课：电脑组装前的准备
第十三课：组装流程
1、电脑硬件设备的组装
2、连接机箱内部连线
3、电脑外部设备的连接
第三章：系统软件的安装
第十四课：轻松设置BIOS
第十五课：计算机软件介绍
第十六课：Windows XP操作系统的安装
第十七课：硬件驱动程序与常用软件的安装
1、硬件驱动程序的安装
2、常用软件的安装
第十八课：电脑上网的安装与设置
1、电脑上网的安装
2、网络连接的建立
第四章：WINXP的基本应用
第十九课： Windows-XP桌面的认识及设置
第二十课：窗口的组成及操作
第二十一课：在Windows XP中输入汉字
第二十二课：Windows-XP资源的管理
第二十三课：Windows-XP中软件的管理
第二十四课：Windows-XP附件程序应用
第二十五课：听音乐、看影片、玩游戏
第二十六课：网上冲浪
1、搜索和下载网络信息
2、申请邮箱与收发邮件
第五章：电脑的维护与故障排除
第二十七课：电脑硬件的日常维护
第二十八课：系统故障诊断
第二十九课：操作系统日常维护与故障排除
第三十课：计算机病毒防范

Hexagonal Image Processing

2008-05-23 08:47:29

he ?eld of image processing has seen many developments in many
fronts since its inception. However, there is a dearth of knowledge
Twhen it comes to one area namely the area of using alternate sam-
pling grids. Almost every textbook on Digital Image Processing mentions the
possibility of using hexagonal sampling grids as an alternative to the conven-
tional square grid. The mention, however, is usually cursory, leading one to
wonder if considering an alternative sampling grid is just a worthless exercise.
Nevertheless, the cursory mention also often includes a positive point about a
hexagonal grid being advantageous for certain types of functions. While it was
curiosity that got us interested in using hexagonal grids, it was the positive
point that spurred us to study the possibility of using such a grid further and
deeper. In this process we discovered that while many researchers have con-
sidered the use of hexagonal grids for image processing, most material on this
topic is available only in the form of research papers in journals or conference
proceedings. In fact it is not possible to ?nd even a comprehensive survey on
this topic in any journal. Hence the motivation for this monograph.
A large part of the work that is reported in the book was carried out when
the authors were at the Department of Electrical and Electronic Engineering,
The University of Auckland, New Zealand. The book took its current shape
and form when the authors had moved on to the University of Southampton
(LM) and IIIT-Hyderabad (JS). Special thanks to Prof. Narendra Ahuja for
readily agreeing to write the foreword. Thanks are due to the anonymous
reviewers whose feedback helped towards making some key improvements to
the book.

Compiler Construction - Lecture Notes in Computer Science

2008-04-26 15:15:14

Editorial Reviews

Product Description

This book constitutes the refereed proceedings of the 13th International Conference on Compiler Construction, CC 2004, held in Barcelona, Spain, in March/April 2004.

The 19 revised full papers presented together with the abstract of an invited talk were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections on program analysis, parsing, loop analysis, optimization, code generation and backend optimizations, and compiler construction.

Table of Contents

Invited Talk
Developing a Foundation for Code Optimization 1
Mary Lou Soffa

Program Analysis

Analyzing Memory Accesses in x86 Executables 5
Gogul Balakrishnan, Thomas Reps

The Limits of Alias Analysis for Scalar Optimizations 24
Rezaul A. Chowdhury, Peter Djeu, Brendon Cahoon,
James H. Burrill, Kathryn S. McKinley

Pruning Interference and Ready Dependence 39
for Slicing Concurrent Java Programs
Venkatesch Prasad Ranganath, John Hatcliff

Data Dependence Profiling for Speculative Optimizations 57
Tong Chen, Jin Lin, Xiaoru Dai, Wei-Chung Hsu, Pen-Chung Yew

Parsing

Elkhound: A Fast, Practical GLR Parser Generator 73
Scott McPeak, George C. Necula

Generalised Parsing: Some Costs 89
Adrian Johnstone, Elizabeth Scott, Giorgios Economopoulos

Loop Analysis

An Automata-Theoretic Algorithm for Counting Solutions
to Presburger Formulas 104
Erin Parker, Siddhartha Chatterjee

A Symbolic Approach to Bernstein Expansion for Program Analysis 120
and Optimization
Philippe Clauss, Irina Tchoupaeva

Periodic Polyhedra 134
Beno?t Meister

Optimization

Region-Based Partial Dead Code Elimination on Predicated Code 150
Qiong Cai, Lin Gao, Jingling Xue

Value-Based Partial Redundancy Elimination 167
Thomas VanDrunen, Antony L. Hosking

Increasing the Applicability of Scalar Replacement 185
Byoungro So, Mary Hall

Reducing the Cost of Object Boxing 202
Tim Owen, Des Watson

Code Generation and Backend Optimizations

FFT Compiler Techniques 217
Stefan Kral, Franz Franchetti, Juergen Lorenz,
Christoph W. Ueberhuber, Peter Wurzinger

Widening Integer Arithmetic 232
Kevin Redwine, Norman Ramsey

Stochastic Bit-Width Approximation Using Extreme Value Theory
for Customizable Processors 250
Emre ?zer, Andy P. Nisbet, David Gregg

Using Multiple Memory Access Instructions for Reducing
Code Size 265
Neil Johnson, Alan Mycroft

Compiler Construction

Integrating the Soot Compiler Infrastructure into an IDE 281
Jennifer Lhoták, Laurie Hendren

Declarative Composition of Stack Frames 298
Christian Lindig, Norman Ramsey

Author Index 313

Advances in Applied Artificial Intelligence

2008-04-17 08:09:40

本书介绍了人工智能的在应用领域的最新进展，与其他讲述人工智能的资料相比，本书注重了人工智能在实际中的应用研究
Chapter I Soft Computing Paradigms and Regression Trees in Decision Support Systems

Chapter II Application of Text Mining Methodologies to Health Insurance Schedules

Chapter III Coordinating Agent Interactions Under Open Environments

Chapter IV Literacy by Way of Automatic Speech Recognition

Chapter V Smart Cars: The Next Frontier

Chapter VI The Application of Swarm Intelligence to Collective Robots

Chapter VII Self-Organising Impact Sensing Networks in Robust Aerospace Vehicles

Chapter VIII Knowledge Through Evolution

Chapter IX Neural Networks for the Classification of Benign and Malignant Patterns in Digital Mammograms

Chapter X Swarm Intelligence and the Taguchi Method for Identification of Fuzzy Models

In Chapter I, Tran, Abraham, and Jain investigate the use of multiple soft computing
techniques such as neural networks, evolutionary algorithms, and fuzzy inference
methods for creating intelligent decision support systems. Their particular emphasis is
on blending these methods to provide a decision support system which is robust, can
learn from the data, can handle uncertainty, and can give some response even in situations
for which no prior human decisions have been made. They have carried out
extensive comparative work with the various techniques on their chosen application,
which is the field of tactical air combat.
In Chapter II, Tsoi, To, and Hagenbuchner tackle a difficult problem in text mining
— automatic classification of documents using only the words in the documents. They
discuss a number of rival and cooperating techniques and, in particular, give a very
clear discussion on latent semantic kernels. Kernel techniques have risen to prominence
recently due to the pioneering work of Vapnik. The application to text mining in
developing kernels specifically for this task is one of the major achievements in this
field. The comparative study on health insurance schedules makes interesting reading.
Bai and Zhang in Chapter III take a very strong position on what constitutes an
agent: “An intelligent agent is a reactive, proactive, autonomous, and social entity”.
Their chapter concentrates very strongly on the last aspect since it deals with multiagent
systems in which the relations between agents is not pre-defined nor fixed when
it is learned. The problems of inter-agent communication are discussed under two
headings: The first investigates how an agent may have knowledge of its world and
what ontologies can be used to specify the knowledge; the second deals with agent
interaction protocols and how these may be formalised. These are set in the discussion
of a supply-chain formation.
Like many of the chapters in this volume, Chapter IV forms almost a mini-book (at
50+ pages), but Gluck and Fulcher give an extensive review of automatic speech recognition
systems covering pre-processing, feature extraction, and pattern matching. The
x
authors give an excellent review of the main techniques currently used including hidden
Markov models, linear predictive coding, dynamic time warping, and artificial neural
networks with the authors’ familiarity with the nuts-and-bolts of the techniques
being evident in the detail with which they discuss each technique. For example, the
artificial neural network section discusses not only the standard back propagation
algorithm and self-organizing maps, but also recurrent neural networks and the related
time-delay neural networks. However, the main topic of the chapter is the review of the
draw-talk-write approach to literacy which has been ongoing research for almost a
decade. Most recent work has seen this technique automated using several of the
techniques discussed above. The result is a socially-useful method which is still in
development but shows a great deal of potential.
Petersson, Fletcher, Barnes, and Zelinsky turn our attention to their Smart Cars
project in Chapter V. This deals with the intricacies of Driver Assistance Systems,
enhancing the driver’s ability to drive rather than replacing the driver. Much of their
work is with monitoring systems, but they also have strong reasoning systems which,
since the work involves keeping the driver in the loop, must be intuitive and explanatory.
The system involves a number of different technologies for different parts of the
system: Naturally, since this is a real-world application, much of the data acquired is
noisy, so statistical methods and probabilistic modelling play a big part in their system,
while support vectors are used for object-classification.
Amanda and Noel Sharkey take a more technique-driven approach in Chapter VI
when they investigate the application of swarm techniques to collective robotics. Many
of the issues such as communication which arise in swarm intelligence mirror those of
multi-agent systems, but one of the defining attributes of swarms is that the individual
components should be extremely simple, a constraint which does not appear in multiagent
systems. The Sharkeys enumerate the main components of such a system as
being composed of a group of simple agents which are autonomous, can communicate
only locally, and are biologically inspired. Each of these properties is discussed in
some detail in Chapter VI. Sometimes these techniques are combined with artificial
neural networks to control the individual agents or genetic algorithms, for example, for
developing control systems. The application to robotics gives a fascinating case-study.
In Chapter VII, the topic of structural health management (SHM) is introduced.
This “is a new approach to monitoring and maintaining the integrity and performance
of structures as they age and/or sustain damage”, and Prokopenko and his co-authors
are particularly interested in applying this to aerospace systems in which there are
inherent difficulties, in that they are operating under extreme conditions. A multi-agent
system is created to handle the various sub-tasks necessary in such a system, which is
created using an interaction between top-down dissection of the tasks to be done with
a bottom-up set of solutions for specific tasks. Interestingly, they consider that most of
the bottom-up development should be based on self-organising principles, which means
that the top-down dissection has to be very precise. Since they have a multi-agent
system, communication between the agents is a priority: They create a system whereby
only neighbours can communicate with one another, believing that this gives robustness
to the whole system in that there are then multiple channels of communication.
Their discussion of chaotic regimes and self-repair systems provides a fascinating
insight into the type of system which NASA is currently investigating. This chapter
places self-referentiability as a central factor in evolving multi-agent systems.
In Chapter VIII, Beale and Pryke make an elegant case for using computer algorithms
for the tasks for which they are best suited, while retaining human input into any
investigation for the tasks for which the human is best suited. In an exploratory data
investigation, for example, it may one day be interesting to identify clusters in a data
set, another day it may be more interesting to identify outliers, while a third day may see
the item of interest shift to the manifold in which the data lies. These aspects are
specific to an individual’s interests and will change in time; therefore, they develop a
mechanism by which the human user can determine the criterion of interest for a specific
data set so that the algorithm can optimise the view of the data given to the human,
taking into account this criterion. They discuss trading accuracy for understanding in
that, if presenting 80% of a solution makes it more accessible to human understanding
than a possible 100% solution, it may be preferable to take the 80% solution. A combination
of evolutionary algorithms and a type of spring model are used to generate
interesting views.
Chapter IX sees an investigation by Verma and Panchal into the use of neural
networks for digital mammography. The whole process is discussed here from collection
of data, early detection of suspicious areas, area extraction, feature extraction and
selection, and finally the classification of patterns into ‘benign’ or ‘malignant’. An
extensive review of the literature is given, followed by a case study on some benchmark
data sets. Finally the authors make a plea for more use of standard data sets, something
that will meet with heartfelt agreement from other researchers who have tried to compare
different methods which one finds in the literature.
In Chapter X, Khosla, Kumar, and Aggarwal report on the application of particle
swarm optimisation and the Taguchi method to the derivation of optimal fuzzy models
from the available data. The authors emphasize the importance of selecting appropriate
PSO strategies and parameters for such tasks, as these impact significantly on performance.
Their approach is validated by way of data from a rapid Ni-Cd battery charger.
As we see, the chapters in this volume represent a wide spectrum of work, and
each is self-contained. Therefore, the reader can dip into this book in any order he/she
wishes. There are also extensive references within each chapter which an interested
reader may wish to pursue, so this book can be used as a central resource from which
major avenues of research may be approached.

模式识别和机器学习 Pattern Recognition and Machine Learning

2008-04-16 22:54:44

1 Introduction 1
1.1 Example: Polynomial Curve Fitting . . . . . . . . . . . . . . . . . 4
1.2 Probability Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.2.1 Probability densities . . . . . . . . . . . . . . . . . . . . . 17
1.2.2 Expectations and covariances . . . . . . . . . . . . . . . . 19
1.2.3 Bayesian probabilities . . . . . . . . . . . . . . . . . . . . 21
1.2.4 The Gaussian distribution . . . . . . . . . . . . . . . . . . 24
1.2.5 Curve fitting re-visited . . . . . . . . . . . . . . . . . . . . 28
1.2.6 Bayesian curve fitting . . . . . . . . . . . . . . . . . . . . 30
1.3 Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
1.4 The Curse of Dimensionality . . . . . . . . . . . . . . . . . . . . . 33
1.5 Decision Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
1.5.1 Minimizing the misclassification rate . . . . . . . . . . . . 39
1.5.2 Minimizing the expected loss . . . . . . . . . . . . . . . . 41
1.5.3 The reject option . . . . . . . . . . . . . . . . . . . . . . . 42
1.5.4 Inference and decision . . . . . . . . . . . . . . . . . . . . 42
1.5.5 Loss functions for regression . . . . . . . . . . . . . . . . . 46
1.6 Information Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 48
1.6.1 Relative entropy and mutual information . . . . . . . . . . 55
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
xiii
xiv CONTENTS
2 Probability Distributions 67
2.1 Binary Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
2.1.1 The beta distribution . . . . . . . . . . . . . . . . . . . . . 71
2.2 Multinomial Variables . . . . . . . . . . . . . . . . . . . . . . . . 74
2.2.1 The Dirichlet distribution . . . . . . . . . . . . . . . . . . . 76
2.3 The Gaussian Distribution . . . . . . . . . . . . . . . . . . . . . . 78
2.3.1 Conditional Gaussian distributions . . . . . . . . . . . . . . 85
2.3.2 Marginal Gaussian distributions . . . . . . . . . . . . . . . 88
2.3.3 Bayes’ theorem for Gaussian variables . . . . . . . . . . . . 90
2.3.4 Maximum likelihood for the Gaussian . . . . . . . . . . . . 93
2.3.5 Sequential estimation . . . . . . . . . . . . . . . . . . . . . 94
2.3.6 Bayesian inference for the Gaussian . . . . . . . . . . . . . 97
2.3.7 Student’s t-distribution . . . . . . . . . . . . . . . . . . . . 102
2.3.8 Periodic variables . . . . . . . . . . . . . . . . . . . . . . . 105
2.3.9 Mixtures of Gaussians . . . . . . . . . . . . . . . . . . . . 110
2.4 The Exponential Family . . . . . . . . . . . . . . . . . . . . . . . 113
2.4.1 Maximum likelihood and sufficient statistics . . . . . . . . 116
2.4.2 Conjugate priors . . . . . . . . . . . . . . . . . . . . . . . 117
2.4.3 Noninformative priors . . . . . . . . . . . . . . . . . . . . 117
2.5 Nonparametric Methods . . . . . . . . . . . . . . . . . . . . . . . 120
2.5.1 Kernel density estimators . . . . . . . . . . . . . . . . . . . 122
2.5.2 Nearest-neighbour methods . . . . . . . . . . . . . . . . . 124
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
3 Linear Models for Regression 137
3.1 Linear Basis Function Models . . . . . . . . . . . . . . . . . . . . 138
3.1.1 Maximum likelihood and least squares . . . . . . . . . . . . 140
3.1.2 Geometry of least squares . . . . . . . . . . . . . . . . . . 143
3.1.3 Sequential learning . . . . . . . . . . . . . . . . . . . . . . 143
3.1.4 Regularized least squares . . . . . . . . . . . . . . . . . . . 144
3.1.5 Multiple outputs . . . . . . . . . . . . . . . . . . . . . . . 146
3.2 The Bias-Variance Decomposition . . . . . . . . . . . . . . . . . . 147
3.3 Bayesian Linear Regression . . . . . . . . . . . . . . . . . . . . . 152
3.3.1 Parameter distribution . . . . . . . . . . . . . . . . . . . . 152
3.3.2 Predictive distribution . . . . . . . . . . . . . . . . . . . . 156
3.3.3 Equivalent kernel . . . . . . . . . . . . . . . . . . . . . . . 159
3.4 Bayesian Model Comparison . . . . . . . . . . . . . . . . . . . . . 161
3.5 The Evidence Approximation . . . . . . . . . . . . . . . . . . . . 165
3.5.1 Evaluation of the evidence function . . . . . . . . . . . . . 166
3.5.2 Maximizing the evidence function . . . . . . . . . . . . . . 168
3.5.3 Effective number of parameters . . . . . . . . . . . . . . . 170
3.6 Limitations of Fixed Basis Functions . . . . . . . . . . . . . . . . 172
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
CONTENTS xv
4 Linear Models for Classification 179
4.1 Discriminant Functions . . . . . . . . . . . . . . . . . . . . . . . . 181
4.1.1 Two classes . . . . . . . . . . . . . . . . . . . . . . . . . . 181
4.1.2 Multiple classes . . . . . . . . . . . . . . . . . . . . . . . . 182
4.1.3 Least squares for classification . . . . . . . . . . . . . . . . 184
4.1.4 Fisher’s linear discriminant . . . . . . . . . . . . . . . . . . 186
4.1.5 Relation to least squares . . . . . . . . . . . . . . . . . . . 189
4.1.6 Fisher’s discriminant for multiple classes . . . . . . . . . . 191
4.1.7 The perceptron algorithm . . . . . . . . . . . . . . . . . . . 192
4.2 Probabilistic Generative Models . . . . . . . . . . . . . . . . . . . 196
4.2.1 Continuous inputs . . . . . . . . . . . . . . . . . . . . . . 198
4.2.2 Maximum likelihood solution . . . . . . . . . . . . . . . . 200
4.2.3 Discrete features . . . . . . . . . . . . . . . . . . . . . . . 202
4.2.4 Exponential family . . . . . . . . . . . . . . . . . . . . . . 202
4.3 Probabilistic Discriminative Models . . . . . . . . . . . . . . . . . 203
4.3.1 Fixed basis functions . . . . . . . . . . . . . . . . . . . . . 204
4.3.2 Logistic regression . . . . . . . . . . . . . . . . . . . . . . 205
4.3.3 Iterative reweighted least squares . . . . . . . . . . . . . . 207
4.3.4 Multiclass logistic regression . . . . . . . . . . . . . . . . . 209
4.3.5 Probit regression . . . . . . . . . . . . . . . . . . . . . . . 210
4.3.6 Canonical link functions . . . . . . . . . . . . . . . . . . . 212
4.4 The Laplace Approximation . . . . . . . . . . . . . . . . . . . . . 213
4.4.1 Model comparison and BIC . . . . . . . . . . . . . . . . . 216
4.5 Bayesian Logistic Regression . . . . . . . . . . . . . . . . . . . . 217
4.5.1 Laplace approximation . . . . . . . . . . . . . . . . . . . . 217
4.5.2 Predictive distribution . . . . . . . . . . . . . . . . . . . . 218
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
5 Neural Networks 225
5.1 Feed-forward Network Functions . . . . . . . . . . . . . . . . . . 227
5.1.1 Weight-space symmetries . . . . . . . . . . . . . . . . . . 231
5.2 Network Training . . . . . . . . . . . . . . . . . . . . . . . . . . . 232
5.2.1 Parameter optimization . . . . . . . . . . . . . . . . . . . . 236
5.2.2 Local quadratic approximation . . . . . . . . . . . . . . . . 237
5.2.3 Use of gradient information . . . . . . . . . . . . . . . . . 239
5.2.4 Gradient descent optimization . . . . . . . . . . . . . . . . 240
5.3 Error Backpropagation . . . . . . . . . . . . . . . . . . . . . . . . 241
5.3.1 Evaluation of error-function derivatives . . . . . . . . . . . 242
5.3.2 A simple example . . . . . . . . . . . . . . . . . . . . . . 245
5.3.3 Efficiency of backpropagation . . . . . . . . . . . . . . . . 246
5.3.4 The Jacobian matrix . . . . . . . . . . . . . . . . . . . . . 247
5.4 The Hessian Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . 249
5.4.1 Diagonal approximation . . . . . . . . . . . . . . . . . . . 250
5.4.2 Outer product approximation . . . . . . . . . . . . . . . . . 251
5.4.3 Inverse Hessian . . . . . . . . . . . . . . . . . . . . . . . . 252
xvi CONTENTS
5.4.4 Finite differences . . . . . . . . . . . . . . . . . . . . . . . 252
5.4.5 Exact evaluation of the Hessian . . . . . . . . . . . . . . . 253
5.4.6 Fast multiplication by the Hessian . . . . . . . . . . . . . . 254
5.5 Regularization in Neural Networks . . . . . . . . . . . . . . . . . 256
5.5.1 Consistent Gaussian priors . . . . . . . . . . . . . . . . . . 257
5.5.2 Early stopping . . . . . . . . . . . . . . . . . . . . . . . . 259
5.5.3 Invariances . . . . . . . . . . . . . . . . . . . . . . . . . . 261
5.5.4 Tangent propagation . . . . . . . . . . . . . . . . . . . . . 263
5.5.5 Training with transformed data . . . . . . . . . . . . . . . . 265
5.5.6 Convolutional networks . . . . . . . . . . . . . . . . . . . 267
5.5.7 Soft weight sharing . . . . . . . . . . . . . . . . . . . . . . 269
5.6 Mixture Density Networks . . . . . . . . . . . . . . . . . . . . . . 272
5.7 Bayesian Neural Networks . . . . . . . . . . . . . . . . . . . . . . 277
5.7.1 Posterior parameter distribution . . . . . . . . . . . . . . . 278
5.7.2 Hyperparameter optimization . . . . . . . . . . . . . . . . 280
5.7.3 Bayesian neural networks for classification . . . . . . . . . 281
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284
6 Kernel Methods 291
6.1 Dual Representations . . . . . . . . . . . . . . . . . . . . . . . . . 293
6.2 Constructing Kernels . . . . . . . . . . . . . . . . . . . . . . . . . 294
6.3 Radial Basis Function Networks . . . . . . . . . . . . . . . . . . . 299
6.3.1 Nadaraya-Watson model . . . . . . . . . . . . . . . . . . . 301
6.4 Gaussian Processes . . . . . . . . . . . . . . . . . . . . . . . . . . 303
6.4.1 Linear regression revisited . . . . . . . . . . . . . . . . . . 304
6.4.2 Gaussian processes for regression . . . . . . . . . . . . . . 306
6.4.3 Learning the hyperparameters . . . . . . . . . . . . . . . . 311
6.4.4 Automatic relevance determination . . . . . . . . . . . . . 312
6.4.5 Gaussian processes for classification . . . . . . . . . . . . . 313
6.4.6 Laplace approximation . . . . . . . . . . . . . . . . . . . . 315
6.4.7 Connection to neural networks . . . . . . . . . . . . . . . . 319
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320
7 Sparse Kernel Machines 325
7.1 Maximum Margin Classifiers . . . . . . . . . . . . . . . . . . . . 326
7.1.1 Overlapping class distributions . . . . . . . . . . . . . . . . 331
7.1.2 Relation to logistic regression . . . . . . . . . . . . . . . . 336
7.1.3 Multiclass SVMs . . . . . . . . . . . . . . . . . . . . . . . 338
7.1.4 SVMs for regression . . . . . . . . . . . . . . . . . . . . . 339
7.1.5 Computational learning theory . . . . . . . . . . . . . . . . 344
7.2 Relevance Vector Machines . . . . . . . . . . . . . . . . . . . . . 345
7.2.1 RVM for regression . . . . . . . . . . . . . . . . . . . . . . 345
7.2.2 Analysis of sparsity . . . . . . . . . . . . . . . . . . . . . . 349
7.2.3 RVM for classification . . . . . . . . . . . . . . . . . . . . 353
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357
CONTENTS xvii
8 Graphical Models 359
8.1 Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 360
8.1.1 Example: Polynomial regression . . . . . . . . . . . . . . . 362
8.1.2 Generative models . . . . . . . . . . . . . . . . . . . . . . 365
8.1.3 Discrete variables . . . . . . . . . . . . . . . . . . . . . . . 366
8.1.4 Linear-Gaussian models . . . . . . . . . . . . . . . . . . . 370
8.2 Conditional Independence . . . . . . . . . . . . . . . . . . . . . . 372
8.2.1 Three example graphs . . . . . . . . . . . . . . . . . . . . 373
8.2.2 D-separation . . . . . . . . . . . . . . . . . . . . . . . . . 378
8.3 Markov Random Fields . . . . . . . . . . . . . . . . . . . . . . . 383
8.3.1 Conditional independence properties . . . . . . . . . . . . . 383
8.3.2 Factorization properties . . . . . . . . . . . . . . . . . . . 384
8.3.3 Illustration: Image de-noising . . . . . . . . . . . . . . . . 387
8.3.4 Relation to directed graphs . . . . . . . . . . . . . . . . . . 390
8.4 Inference in Graphical Models . . . . . . . . . . . . . . . . . . . . 393
8.4.1 Inference on a chain . . . . . . . . . . . . . . . . . . . . . 394
8.4.2 Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398
8.4.3 Factor graphs . . . . . . . . . . . . . . . . . . . . . . . . . 399
8.4.4 The sum-product algorithm . . . . . . . . . . . . . . . . . . 402
8.4.5 The max-sum algorithm . . . . . . . . . . . . . . . . . . . 411
8.4.6 Exact inference in general graphs . . . . . . . . . . . . . . 416
8.4.7 Loopy belief propagation . . . . . . . . . . . . . . . . . . . 417
8.4.8 Learning the graph structure . . . . . . . . . . . . . . . . . 418
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418
9 Mixture Models and EM 423
9.1 K-means Clustering . . . . . . . . . . . . . . . . . . . . . . . . . 424
9.1.1 Image segmentation and compression . . . . . . . . . . . . 428
9.2 Mixtures of Gaussians . . . . . . . . . . . . . . . . . . . . . . . . 430
9.2.1 Maximum likelihood . . . . . . . . . . . . . . . . . . . . . 432
9.2.2 EM for Gaussian mixtures . . . . . . . . . . . . . . . . . . 435
9.3 An Alternative View of EM . . . . . . . . . . . . . . . . . . . . . 439
9.3.1 Gaussian mixtures revisited . . . . . . . . . . . . . . . . . 441
9.3.2 Relation to K-means . . . . . . . . . . . . . . . . . . . . . 443
9.3.3 Mixtures of Bernoulli distributions . . . . . . . . . . . . . . 444
9.3.4 EM for Bayesian linear regression . . . . . . . . . . . . . . 448
9.4 The EM Algorithm in General . . . . . . . . . . . . . . . . . . . . 450
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455
10 Approximate Inference 461
10.1 Variational Inference . . . . . . . . . . . . . . . . . . . . . . . . . 462
10.1.1 Factorized distributions . . . . . . . . . . . . . . . . . . . . 464
10.1.2 Properties of factorized approximations . . . . . . . . . . . 466
10.1.3 Example: The univariate Gaussian . . . . . . . . . . . . . . 470
10.1.4 Model comparison . . . . . . . . . . . . . . . . . . . . . . 473
10.2 Illustration: Variational Mixture of Gaussians . . . . . . . . . . . . 474
xviii CONTENTS
10.2.1 Variational distribution . . . . . . . . . . . . . . . . . . . . 475
10.2.2 Variational lower bound . . . . . . . . . . . . . . . . . . . 481
10.2.3 Predictive density . . . . . . . . . . . . . . . . . . . . . . . 482
10.2.4 Determining the number of components . . . . . . . . . . . 483
10.2.5 Induced factorizations . . . . . . . . . . . . . . . . . . . . 485
10.3 Variational Linear Regression . . . . . . . . . . . . . . . . . . . . 486
10.3.1 Variational distribution . . . . . . . . . . . . . . . . . . . . 486
10.3.2 Predictive distribution . . . . . . . . . . . . . . . . . . . . 488
10.3.3 Lower bound . . . . . . . . . . . . . . . . . . . . . . . . . 489
10.4 Exponential Family Distributions . . . . . . . . . . . . . . . . . . 490
10.4.1 Variational message passing . . . . . . . . . . . . . . . . . 491
10.5 Local Variational Methods . . . . . . . . . . . . . . . . . . . . . . 493
10.6 Variational Logistic Regression . . . . . . . . . . . . . . . . . . . 498
10.6.1 Variational posterior distribution . . . . . . . . . . . . . . . 498
10.6.2 Optimizing the variational parameters . . . . . . . . . . . . 500
10.6.3 Inference of hyperparameters . . . . . . . . . . . . . . . . 502
10.7 Expectation Propagation . . . . . . . . . . . . . . . . . . . . . . . 505
10.7.1 Example: The clutter problem . . . . . . . . . . . . . . . . 511
10.7.2 Expectation propagation on graphs . . . . . . . . . . . . . . 513
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517
11 Sampling Methods 523
11.1 Basic Sampling Algorithms . . . . . . . . . . . . . . . . . . . . . 526
11.1.1 Standard distributions . . . . . . . . . . . . . . . . . . . . 526
11.1.2 Rejection sampling . . . . . . . . . . . . . . . . . . . . . . 528
11.1.3 Adaptive rejection sampling . . . . . . . . . . . . . . . . . 530
11.1.4 Importance sampling . . . . . . . . . . . . . . . . . . . . . 532
11.1.5 Sampling-importance-resampling . . . . . . . . . . . . . . 534
11.1.6 Sampling and the EM algorithm . . . . . . . . . . . . . . . 536
11.2 Markov Chain Monte Carlo . . . . . . . . . . . . . . . . . . . . . 537
11.2.1 Markov chains . . . . . . . . . . . . . . . . . . . . . . . . 539
11.2.2 The Metropolis-Hastings algorithm . . . . . . . . . . . . . 541
11.3 Gibbs Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . 542
11.4 Slice Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 546
11.5 The Hybrid Monte Carlo Algorithm . . . . . . . . . . . . . . . . . 548
11.5.1 Dynamical systems . . . . . . . . . . . . . . . . . . . . . . 548
11.5.2 Hybrid Monte Carlo . . . . . . . . . . . . . . . . . . . . . 552
11.6 Estimating the Partition Function . . . . . . . . . . . . . . . . . . 554
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 556
12 Continuous Latent Variables 559
12.1 Principal Component Analysis . . . . . . . . . . . . . . . . . . . . 561
12.1.1 Maximum variance formulation . . . . . . . . . . . . . . . 561
12.1.2 Minimum-error formulation . . . . . . . . . . . . . . . . . 563
12.1.3 Applications of PCA . . . . . . . . . . . . . . . . . . . . . 565
12.1.4 PCA for high-dimensional data . . . . . . . . . . . . . . . 569
CONTENTS xix
12.2 Probabilistic PCA . . . . . . . . . . . . . . . . . . . . . . . . . . 570
12.2.1 Maximum likelihood PCA . . . . . . . . . . . . . . . . . . 574
12.2.2 EM algorithm for PCA . . . . . . . . . . . . . . . . . . . . 577
12.2.3 Bayesian PCA . . . . . . . . . . . . . . . . . . . . . . . . 580
12.2.4 Factor analysis . . . . . . . . . . . . . . . . . . . . . . . . 583
12.3 Kernel PCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 586
12.4 Nonlinear Latent Variable Models . . . . . . . . . . . . . . . . . . 591
12.4.1 Independent component analysis . . . . . . . . . . . . . . . 591
12.4.2 Autoassociative neural networks . . . . . . . . . . . . . . . 592
12.4.3 Modelling nonlinear manifolds . . . . . . . . . . . . . . . . 595
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 599
13 Sequential Data 605
13.1 Markov Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607
13.2 Hidden Markov Models . . . . . . . . . . . . . . . . . . . . . . . 610
13.2.1 Maximum likelihood for the HMM . . . . . . . . . . . . . 615
13.2.2 The forward-backward algorithm . . . . . . . . . . . . . . 618
13.2.3 The sum-product algorithm for the HMM . . . . . . . . . . 625
13.2.4 Scaling factors . . . . . . . . . . . . . . . . . . . . . . . . 627
13.2.5 The Viterbi algorithm . . . . . . . . . . . . . . . . . . . . . 629
13.2.6 Extensions of the hidden Markov model . . . . . . . . . . . 631
13.3 Linear Dynamical Systems . . . . . . . . . . . . . . . . . . . . . . 635
13.3.1 Inference in LDS . . . . . . . . . . . . . . . . . . . . . . . 638
13.3.2 Learning in LDS . . . . . . . . . . . . . . . . . . . . . . . 642
13.3.3 Extensions of LDS . . . . . . . . . . . . . . . . . . . . . . 644
13.3.4 Particle filters . . . . . . . . . . . . . . . . . . . . . . . . . 645
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 646
14 Combining Models 653
14.1 Bayesian Model Averaging . . . . . . . . . . . . . . . . . . . . . . 654
14.2 Committees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655
14.3 Boosting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657
14.3.1 Minimizing exponential error . . . . . . . . . . . . . . . . 659
14.3.2 Error functions for boosting . . . . . . . . . . . . . . . . . 661
14.4 Tree-based Models . . . . . . . . . . . . . . . . . . . . . . . . . . 663
14.5 Conditional Mixture Models . . . . . . . . . . . . . . . . . . . . . 666
14.5.1 Mixtures of linear regression models . . . . . . . . . . . . . 667
14.5.2 Mixtures of logistic models . . . . . . . . . . . . . . . . . 670
14.5.3 Mixtures of experts . . . . . . . . . . . . . . . . . . . . . . 672
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 674
Appendix A Data Sets 677
Appendix B Probability Distributions 685
Appendix C Properties of Matrices 695
xx CONTENTS
Appendix D Calculus of Variations 703
Appendix E Lagrange Multipliers 707
References 711
Index 729

并行计算结构·算法·编程

2008-04-16 15:10:30

【作者】陈国良
【出版社】高等教育出版社【书号】 7040133075
【出版日期】 2003 年8月【开本】 16开【页码】 300 【版次】2-1

【内容简介】
　　本书是教育部“高等教育面向21世纪教学内容和课程体系改革计划”的研究成果，是面向21世纪课程教材和教育部理科计算机应用“九五”规划教材。
　　本书以并行计算为主题，主要讨论并行计算的硬件基础——当代并行计算机系统及其结构模型，并行计算的核心内容——并行算法设计与并行数值算法以及并行计算的软件支持——并行程序的设计原理与方法。本书强调融并行机结构、并行算法和并行编程为一体，着重讨论并行算法的设计方法和并行数值计算算法，力图反映本学科的最新成就和发展趋势。
　　全书共十五章，分为四篇：第一篇包括并行计算机的系统结构模型，当代对称多处理机、大规模并行处理机、机群系统和并行计算的性能评测；第二篇包括并行算法的一般设计策略、基本设计技术和一般设计过程；第三篇包括矩阵运算、稠密与稀疏线性方程组的求解和快速傅里叶变换；第四篇包括并行程序设计基础、共享存储与分布存储系统并行编程以及并行程序设计环境与工具。
　从并行计算的角度，本书体系完整，内容丰富，取材新颖，可作为高等学校计算机及相关专业的本科高年级学生和研究生的教学用书，也可供计算科学与工程(Computational Science and Engineering)学科的研究生和科技人员阅读参考。

【目录信息】
第一篇　并行计算硬件基础
第一章　　并行计算机系统及其结构模型　　　　3　　　　　　 
　1.1　　并行计算　　　　4　　　　　　
　　1.1.1　　并行计算与计算科学　　　　4　　　　　　
　　1.1.2　　当代科学与工程问题的计算需求　　　　4　　　　　　　
1.2　　并行计算机系统互连　　　　8　　　　　　
　　1.2.1　　系统互连　　　　8　　　　　　
　　1.2.2　　静态互连网络　　　　9　　　　　　
　　1.2.3　　动态互连网络　　　　13　　　　　　
　　1.2.4　　标准互连网络　　　　17　　　　　　
　1.3　　并行计算机系统结构　　　　22　　　　　　
　　1.3.1　　并行计算机结构模型　　　　22　　　　　　
　　1.3.2　　并行计算机访存模型　　　　26　　　　　　
　　*1.3.3　并行计算机存储组织　　　　30　　　　　　
　1.4　　小结和导读　　　　34　　　　　　
　习题　　　　35　　　　　　

第二章　　当代并行计算机系统介绍　　　　39　　　　　　
　2.1　　共享存储多处理机系统　　　　40　　　　　　
　　2.1.1　　对称多处理机SMP结构特性　　　　40
......

Artificial Intelligence - A Modern Approach 2nd Edition

2008-04-16 10:54:08

Amazon.com
Artificial Intelligence: A Modern Approach introduces basic ideas in artificial intelligence from the perspective of building intelligent agents, which the authors define as "anything that can be viewed as perceiving its environment through sensors and acting upon the environment through effectors." This textbook is up-to-date and is organized using the latest principles of good textbook design. It includes historical notes at the end of every chapter, exercises, margin notes, a bibliography, and a competent index. Artificial Intelligence: A Modern Approach covers a wide array of material, including first-order logic, game playing, knowledge representation, planning, and reinforcement learning. --This text refers to an out of print or unavailable edition of this title.

Review

"The publication of this textbook was a major step forward, not only for the teaching of AI, but for the unified view of the field that this book introduces. Even for experts in the field, there are important insights in almost every chapter." — Prof. Thomas Dietterich, Oregon State

"Just terrific. The book I've always been waiting for...the AI bible for the next decade." — Prof. Gerd Brewka (Vienna)

"A marvelous achievement, a truly beautiful book!" — Prof. Selmer Bringsjord, RPI

"It's a great book, with incredible breadth and depth, and very well-written. Everyone I know who has used it in their class has loved it." — Prof. Haym Hirsh, Rutgers

"I am deeply impressed by its unprecedented quality in presenting a coherent, balanced, broad and deep, enjoyable picture of the field of AI. It will become tire standard text for the years to come." — Prof. Wolfgang Bibel, Darmstadt

"Terrific! Well-written and well-organised, with comprehensive coverage of the material that every AI student should know." — Prof. Martha Pollack (Michigan)

"Outstanding ...Its descriptions are extremely clear and readable; its organization is excellent; its examples are motivating; and its coverage is scholarly and throughout! ...will deservedly dominate the field for some time." — Prof. Nils Nilsson, Stanford

"The best book available now...It's almost as good as the book Charniak and I wrote, but more up to date. (Okay I'll admit it, it may even be better than our book.)" — Prof. Drew McDermott, Yale

"A magisterial wide scope account of the entire field of Artificial Intelligence that will enlighten professors as well as students." — Dr. Alan Kay

"This is the book that made me love AI." — Student (Indonesia)

A Beautiful Math

2008-04-01 11:01:42

约翰纳什赢得了1994年诺贝尔经济学奖，为开拓性研究，发表在20世纪50年代上一个新的数学分支称为博弈论。在当时的纳什的早期工作，博弈论是简单地受欢迎，有些数学家和冷战分析师。但它仍较为模糊，直到20世纪70年代，当进化生物学家开始觉得有用的。在20世纪80年代经济学家开始拥抱博弈论。此后博弈论的数学找到了一个不断扩大的曲目申请当中广泛的科学学科。

今天神经学家同伴到游戏玩家开动脑筋，人类学家玩的人从原始文化，生物学家们利用游戏来解释的演变人类的语言，和数学家利用游戏，以更好地了解社会网络。

一个共同的螺纹连接很多这样的研究是其相关性，以古谋求建立一个科学的人的社会行为，或"工作守则，不变质" ，在精神上来，把虚构的科学psychohistory形容，在著名的小说基金会由已故艾萨克asimov 。在一个美丽的数学，著名科学作家汤姆和解决方案描述了如何博弈论联系起来，生命科学，社会科学和物理科学，在某种程度上可能对人体带来asimov的梦想更加接近现实。

John Nash won the 1994 Nobel Prize in economics for pioneering research published in the 1950s on a new branch of mathematics known as game theory. At the time of Nash's early work, game theory was briefly popular among some mathematicians and Cold War analysts. But it remained relatively obscure until the 1970s, when evolutionary biologists began to find it useful. In the 1980s economists began to embrace game theory. Since then game theory math has found an ever expanding repertoire of applications among a wide range of scientific disciplines.

Today neuroscientists peer into game players' brains, anthropologists play games with people from primitive cultures, biologists use games to explain the evolution of human language, and mathematicians exploit games to better understand social networks.

A common thread connecting much of this research is its relevance to the ancient quest for a science of human social behavior, or "a Code of Nature," in the spirit of the fictional science of psychohistory described in the famous Foundation novels by the late Isaac Asimov. In A Beautiful Math, acclaimed science writer Tom Siegfried describes how game theory links the life sciences, social sciences and physical sciences in a way that may bring Asimov's dream closer to reality.

计算机科学与技术方法论( 高清pdf完整版)

2008-03-31 03:10:59

对于要系统学计算机的人来说，本书是不可多得的教材，可以让你轻松地进入计算机领域而不至于迷茫，无论初学者还是有一定专业知识的人都应该仔细的看遍本书，或许你会恍然大悟，强烈推荐！本书的有关内容是受到CCC2002的高度重视的。
本书是作者多年来对计算学科方法论研究成果的总结。作者根据《计算作为一门学科》报告对整个计算学科综述性导引课程的严密性和挑战性的要求，借鉴了数学的公理化思想，对计算学科的主要内容进行了系统化、逻辑化的概括，并通过大量实例，深入浅出地阐明了计算学科中各主领域发展的基本规律，揭示了各主领域之间的内在联系，有助于人们对计算学科的深入了解。
　　本书的主要内容包括：计算机科学与技术方法论的构建，计算学科的历史、定义、根本问题，计算学科各主领域的基本问题，计算学科中的抽象、理论和设计3个学科形态，计算学科中的核心概念、数学方法、系统科学方法、形式化技术、社会和职业的问题等。为了使读者能更好地理解和掌握书中的内容，在各章末还附有一定数量的思考题。
　　本书是计算学科认知领域的一本学术专著，也可作为高等院校计算学科方法论、计算机导论等课程的教材或参考书，还可供相关专业的学生、教师和科技人员参考。

Multimedia Fingerprinting Forensics for Traitor Tracing

2008-03-28 12:24:54

Preface xi
1. Introduction 1
2. Preliminaries on data embedding 7
2.1. Content protection via digital watermarking 7
2.1.1. Major applications and design requirements 8
2.1.2. Basic embedding approaches 9
2.2. Robust additive spread-spectrum embedding 11
2.2.1. Overview of spread-spectrum embedding 12
2.2.2. Distortion and attacks against robust embedding 13
2.2.3. Mathematical formulation 15
2.2.4. Alternative detection statistics 17
2.2.5. Exploiting human visual properties 21
2.3. Employing spread-spectrum embedding in fingerprinting 23
3. Collusion attacks 25
3.1. Introduction to collusion attacks 26
3.1.1. Linear collusion attacks 26
3.1.2. Nonlinear collusion attacks 28
3.2. Introduction to order statistics 29
3.2.1. Distribution of order statistics 30
3.2.2. Joint distribution of two different order statistics 30
3.2.3. Joint distribution of order statistics and
the unordered random variables 31
3.3. Multimedia fingerprinting system model 33
3.3.1. Fingerprinting systems and collusion attacks 33
3.3.2. Performance criteria 35
3.4. Statistical analysis of collusion attacks 36
3.4.1. Analysis of collusion attacks 36
3.4.2. Analysis of detection statistics 41
3.4.3. System performance analysis 42
3.5. Collusion attacks on Gaussian-based fingerprints 43
3.5.1. Unbounded Gaussian fingerprints 43
3.5.2. Bounded Gaussian-like fingerprints 48
3.6. Preprocessing of the extracted fingerprints 52
3.7. Experiments with images 57
3.8. Chapter summary 61
A print edition of this book can be purchased at
http://www.hindawi.com/spc.4.html
http://www.amazon.com/dp/9775945186
viii Contents
4. Orthogonal fingerprinting and collusion resistance 63
4.1. Collusion resistance analysis 65
4.1.1. The maximum detector 66
4.1.2. The thresholding detector 68
4.2. Extensions to other performance criteria 78
4.3. Extensions to other types of attacks 83
4.4. A practical estimator for the amount of colluders 88
4.5. Experiments with images 90
4.6. Efficient fingerprint detection using tree structure 94
4.6.1. Tree-structured detection strategy 94
4.6.2. Experiments on tree-based detector 98
4.7. Chapter summary 99
5. Group-oriented fingerprinting 101
5.1. Motivation for group-based fingerprinting 102
5.2. Two-tier group-oriented fingerprinting system 105
5.2.1. Fingerprint design scheme 105
5.2.2. Detection scheme 106
5.2.3. Performance analysis 111
5.3. Tree-structure-based fingerprinting system 121
5.3.1. Fingerprint design scheme 121
5.3.2. Detection scheme 122
5.3.3. Parameter settings and performance analysis 124
5.4. Experimental results on images 132
5.5. Chapter summary 135
6. Anticollusion-coded (ACC) fingerprinting 137
6.1. Prior work on collusion-resistant fingerprinting for generic data 139
6.2. Code modulation with spread-spectrum embedding 142
6.3. Combinatorial designs 143
6.4. Combinatorial-design-based anticollusion codes 148
6.4.1. Formulation and construction of ACC codes 149
6.4.2. Examples of BIBD-based ACC 150
6.4.3. ACC coding efficiency and BIBD design methods 152
6.5. Detection strategies and performance tradeoffs 154
6.5.1. Hard detection 156
6.5.2. Adaptive sorting approach 157
6.5.3. Sequential algorithm 157
6.6. Experimental results for ACC fingerprinting 158
6.6.1. ACC simulations with Gaussian signals 158
6.6.2. ACC experiments with images 163
6.7. A unified formulation on fingerprinting strategies 164
6.8. Chapter summary 168
7. Secure fingerprint multicast for video streaming 171
7.1. Secure video streaming 172
A print edition of this book can be purchased at
http://www.hindawi.com/spc.4.html
http://www.amazon.com/dp/9775945186
Contents ix
7.2. Prior art in secure fingerprint multicast 173
7.3. General fingerprint multicast distribution scheme 174
7.4. Joint fingerprint design and distribution scheme 176
7.4.1. Comparison of fingerprint modulation schemes 177
7.4.2. Joint fingerprint design and distribution 180
7.4.3. Addressing the computation constraints 185
7.5. Analysis of bandwidth efficiency 186
7.5.1. “Multicast only” scenario 186
7.5.2. General fingerprint multicast scheme 187
7.5.3. Joint fingerprint design and distribution scheme 191
7.6. Robustness of the embedded fingerprints 194
7.6.1. Digital fingerprinting system model 194
7.6.2. Performance criteria 195
7.6.3. Comparison of collusion resistance 195
7.7. Fingerprint drift compensation 199
7.8. Chapter summary 202
8. Fingerprinting curves 205
8.1. Introduction 205
8.2. Basic embedding and detection 208
8.2.1. Feature extraction 208
8.2.2. Fingerprinting in the control-point domain 210
8.2.3. Fidelity and robustness considerations 212
8.2.4. Experiments with simple curves 215
8.3. Iterative alignment-minimization algorithm for
robust fingerprint detection 219
8.3.1. Problem formulation 221
8.3.2. Iterative alignment-minimization algorithm 222
8.3.3. Detection example and discussion 225
8.4. Experiments with maps 228
8.5. Chapter summary 237
Bibliography 239

UWB Communication Systems-A Comprehensive Overview(超清晰板)

2008-03-28 12:09:45

Introduction1
1.1. Introduction1
1.2. UWBbasics2
1.3. Regulatorybodies4
1.4. ApplicationsofUWB9
1.5. Impulseradioschemes10
1.6. Multicarrierschemes14
1.7. Conclusions17
2. UWBpropagationchannels21
2.1. Introduction21
2.2. Measurementtechniques26
2.3. Propagationeffects 40
2.4. Pathlossandshadowing57
2.5. Delaydispersionandsmall-scalefading67
2.6. Standardizedchannelmodels87
2.7. Body-areanetworks94
2.8. Channelestimationtechniques118
3. Signalprocessing143
3.1. Introduction143
3.2. Impulseradioschemes144
3.3. Multicarrierschemes147
3.4. Pulseshapes150
3.5. Datamodulation157
3.6. Spectrumrandomisationandmultipleaccess167
3.7. Synchronisation175
3.8. Impulseradiodemonstratorfor4-PPM181
3.9. Conclusion200
4. Higher-layerissues:adhocandsensornetworks205
4.1. Introduction205
4.2. Power-efficient UWBnetworks206
4.3. Location-awareUWBnetworks209
4.4. Power-efficient andlocation-awaremediumaccesscontroldesign219
4.5. Performanceanalysisinspecifictestcases222
5. SpatialaspectsofUWB253
5.1. Introduction253
5.2. Amodelfortheultra-widebandspace-variantindoor
multipathradiochannel254
5.3. UWBantennaarrays269
5.4. UWBpolarizationdiversity281
5.5. Spatialdiversity302
5.6. UWBbeamformingandDOAestimation330
5.7. PerformanceanalysisofmultiantennaUWB
wirelesscommunications353
5.8. ChannelcapacityofMIMOUWBindoorwirelesssystems376
6. UWBranging411
6.1. Introduction411
6.2. UWBlocationsystemtechniques,architectures,andanalysis412
6.3. ComparisonofUWBandalternativeradio-basedsystems418
6.4. AtypicalRFlinkbudgetforUWBpositioningsystems421
6.5. Characteristicsofafine-grainedUWBpositioningsystem423
6.6. Positioningtechniquesinharshenvironments426
6.7. UWBpreciserangingwithanexperimentalantenna-arraysystem429
6.8. SystemsintegrationandUWBpositioningtechnology440
7. Regulationandstandardization447
7.1. Introduction447
7.2. Regulation448
7.3. Standardization459
7.4. Coexistencewithradiosystems471

Smart Antennas-State of the Art

2008-03-28 11:34:20

Part I. Receiver
1. Introduction, Wolfgang Utschick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Spatiotemporal interference rejection combining,
David Ast′ely and Bj¨orn Ottersten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3. Subspace methods for space-time processing,
M. Nicoli and U. Spagnolini . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4. Multiuser MIMO channel equalization,
Christoph F. Mecklenbr¨auker, Joachim Wehinger, Thomas Zemen,
Harold Art′es, and Franz Hlawatsch . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5. Joint antenna combining and multiuser detection,
Ralf M¨uller and Laura Cottatellucci . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
6. Synchronization for MIMO systems,
Frederik Simoens, Henk Wymeersch, Heidi Steendam,
and Marc Moeneclaey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
7. Iterative (turbo) signal processing techniques for MIMO signal
detection and equalization, Tad Matsumoto . . . . . . . . . . . . . . . . . . . 119
8. Architectures for reference-based and blind multilayer
detection, Karl-Dirk Kammeyer, J¨urgen Rinas,
and Dirk W¨ubben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
9. Uplink robust adaptive beamforming, Alex B. Gershman . . . . . . . 173
10. Robust and reduced-rank space-time decision feedback
equalization, Frank A. Dietrich, Guido Dietl,
Michael Joham, and Wolfgang Utschick . . . . . . . . . . . . . . . . . . . . . . . . 189
Part II. Channel
11. Introduction, J. Bach Andersen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
12. Propagation, P. Vainikainen, J. Kivinen,
X. Zhao, and H. El-Sallabi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
13. Multidimensional high-resolution channel sounding
measurement, Reiner S. Thom¨a, Markus Landmann,
Andreas Richter, and Uwe Trautwein . . . . . . . . . . . . . . . . . . . . . . . . . . 241
14. MIMO channel models, Kai Yu,
Mats Bengtsson, and Bj¨orn Ottersten . . . . . . . . . . . . . . . . . . . . . . . . . . 271
15. Channel estimation, Geert Leus and Alle-Jan van der Veen . . . . . . . 293
16. Direction-of-arrival estimation, Mats Viberg . . . . . . . . . . . . . . . . . . 321
A print edition of this book can be purchased at
http://www.hindawi.com/spc.3.html
http://www.amazon.com/dp/9775945097
vi Contents
Part III. Transmitter
17. Introduction, Javier Rodr′?guez Fonollosa . . . . . . . . . . . . . . . . . . . . . . 345
18. Unified design of linear transceivers for MIMO channels,
Daniel P′erez Palomar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349
19. Space-time block coding using channel side information,
George J¨ongren, Mikael Skoglund, and Bj¨orn Ottersten . . . . . . . . . . . 375
20. Ordered spatial Tomlinson-Harashima precoding,
Michael Joham and Wolfgang Utschick . . . . . . . . . . . . . . . . . . . . . . . . 401
21. Transmission strategies for the MIMO MAC,
Eduard A. Jorswieck . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423
22. Transmitting over ill-conditioned MIMO channels:
from spatial to constellation multiplexing,
David Gesbert and Jabran Akhtar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443
Part IV. Network Theory
23. Introduction, Holger Boche . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465
24. MIMO channel capacity and measurements,
Andreas F. Molisch and Fredrik Tufvesson . . . . . . . . . . . . . . . . . . . . . . 467
25. Distributed space-time coding techniques for multihop
networks, Sergio Barbarossa, Gesualdo Scutari,
and Loreto Pescosolido . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491
26. Towards a better understanding of the QoS tradeoff
in multiuser multiple-antenna systems,
Slawomir Stanczak and Holger Boche . . . . . . . . . . . . . . . . . . . . . . . . . . 521
27. Duality theory for uplink and downlink multiuser
beamforming, Holger Boche and Martin Schubert . . . . . . . . . . . . . . 545
28. Scheduling in multiple-antenna multiple-access channel,
Holger Boche, Marcin Wiczanowski, and Thomas Haustein . . . . . . . 577
Part V. Technology
29. Technology, Andr′e Bourdoux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615
30. Antenna design for multiantenna systems,
Christian Waldschmidt, Werner S¨orgel, and Werner Wiesbeck . . . . . 617
31. Radio architectures for multiple-antenna systems, D. Evans . . . . . 641
32. Transceiver nonidealities in multiantenna systems,
Andr′e Bourdoux and Jian Liu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 651
33. Multiple antennas for 4G wireless systems,
Franc?ois Horlin, Frederik Petr′e, Eduardo Lopez-Estraviz,
and Frederik Naessens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683
34. Demonstrators and testbeds, Andreas Burg and Markus Rupp . . . 705
A print edition of this book can be purchased at
http://www.hindawi.com/spc.3.html
http://www.amazon.com/dp/9775945097
Contents vii
Part VI. Applications and Systems
35. Introduction, Thomas Kaiser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 727
36. Smart antenna solutions for UMTS,
Andreas Czylwik, Armin Dekorsy, and Batu Chalise . . . . . . . . . . . . . 729
37. UMTS link-level demonstrations with smart antennas,
Klemens Freudenthaler, Mario Huemer, Linus Maurer,
Steffen Paul, and Markus Rupp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 759
38. MIMO systems for the HSDPA FDD mode UMTS service,
Alba Pag`es-Zamora and Markku J. Heikkil¨a . . . . . . . . . . . . . . . . . . . . 787
39. A MIMO platform for research and education,
T. Kaiser, A. Wilzeck, M. Berentsen, A. Camargo, X. Peng,
L. H¨aring, S. Bieder, D. Omoke, A. Kani,O. Lazar, R. Tempel,
and F. Ancona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 811
40. Real-time prototyping of broadband MIMOWLAN systems,
Maryse Wouters and Tom Huybrechts . . . . . . . . . . . . . . . . . . . . . . . . . 853

The Elements of Statistical Learning

2008-03-24 16:25:34

极好的统计机器学习的教材.从原书扫描得到黑白pdf版本,文件比较大.之前有人上传了一个djvu版本,但那个版本唯一问题是中间缺了几页.这个版本是全的.

During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book descibes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learing (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

【内容简介】
计算机信息技术的飞速发展带来了医学、生物学、财经和营销等诸多领域的海量数据。理解这些数据是一种挑战，这导致了统计学领域新工具的发展，并延伸到诸如数据挖掘、机器学习和生物信息学等新领域。许多工具都具有共同的基础，但常常用不同的术语来表达。本书介绍了这些领域的一些重要概念。尽管应用的是统计学方法，但强调的是概念，而不是数学。许多例子附以彩图。本书内容广泛，从有指导的学习（预测）到无指导的学习，应有尽有。包括神经网络、支持向量机、分类树和提升等主题，是同类书籍中介绍得最全面的。

Pattern Recognition and Machine Learning

2008-03-22 00:00:38

一本模式识别和机器学习领域较新的书. 其作者是经典书籍"Neural Networks for Pattern Recognition"的作者. 写作风格是通过清晰的思考和适当的实例来使读者尽可能达到理解.

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.

This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

Genomic Signal Processing and Statistics(高清晰板)

2008-03-21 16:11:04

Part I. Sequence Analysis
1. Representation and analysis of DNA sequences,
Paul Dan Cristea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Part II. Signal Processing and StatisticsMethodologies
in Gene Selection
2. Gene feature selection, Ioan Tabus and Jaakko Astola . . . . . . . . . . . . 67
3. Classification, Ulisses Braga-Neto and Edward R. Dougherty . . . . . . 93
4. Clustering: revealing intrinsic dependencies in microarray data,
Marcel Brun, Charles D. Johnson, and Kenneth S. Ramos . . . . . . . . 129
5. From biochips to laboratory-on-a-chip system, Lei Wang,
Hongying Yin, and Jing Cheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
Part III. Modeling and Statistical Inference of Genetic
Regulatory Networks
6. Modeling and simulation of genetic regulatory networks
by ordinary differential equations,
Hidde de Jong and Johannes Geiselmann . . . . . . . . . . . . . . . . . . . . . . . 201
7. Modeling genetic regulatory networks with probabilistic
Boolean networks, Ilya Shmulevich and Edward R. Dougherty . . . 241
8. Bayesian networks for genomic analysis, Paola Sebastiani,
Maria M. Abad, and Marco F. Ramoni . . . . . . . . . . . . . . . . . . . . . . . . 281
9. Statistical inference of transcriptional regulatory networks,
Xiaodong Wang, Dimitris Anastassiou, and Dong Guo . . . . . . . . . . . 321
Part IV. Array Imaging, Signal Processing in Systems Biology,
and Applications in Disease Diagnosis and Treatments
10. Compressing genomic and proteomic array images for
statistical analyses, Rebecka J¨ornsten and Bin Yu . . . . . . . . . . . . . . . . 341
11. Cancer genomics, proteomics, and clinic applications,
X. Steve Fu, Chien-an A. Hu, Jie Chen, Z. Jane Wang,
and K. J. Ray Liu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367
12. Integrated approach for computational systems biology,
Seungchan Kim, Phillip Stafford, Michael L. Bittner,
and Edward B. Suh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409

电脑史话（新版）

2008-03-18 12:05:52

电脑史话（新版）

● 1、计算机始祖　　　　● 36、苹果穿雨衣
● 2、第一抹曙光　　　　● 37、三比二要好
● 3、编织的程序　　　　● 38、ＰＣ新霸主
● 4、失败的英雄　　　　● 39、龙梦变成真
● 5、携手共赴难　　　　● 40、窗含千秋雪
● 6、穿孔制表机　　　　● 41、最长的工程
● 7、继往与开来　　　　● 42、平地太阳风
● 8、真空驯电子　　　　● 43、四比三更好
● 9、电脑创世记　　　　● 44、任天堂崛起
● 10、千秋电脑父　　　　● 45、英雄出少年
● 11、电脑群英谱　　　　● 46、软件起风云
● 12、巨人的秘密　　　　● 47、微软登王座
● 13、二战建奇勋　　　　● 48、好风凭借力
● 14、璀灿双子星　　　　● 49、硬盘与软盘
● 15、成功的预言　　　　● 50、高级的语言
● 16、父子兵上阵　　　　● 51、语言的革命
● 17、圣诞献厚礼　　　　● 52、键盘与鼠标
● 18、单骑斗巨人　　　　● 53、桌面出版者
● 19、硅谷的诞生　　　　● 54、下棋的机器
● 20、天才八叛逆　　　　● 55、ＡＩ的旗帜
● 21、同时的发明　　　　● 56、机器数学家
● 22、五十亿赌注　　　　● 57、电脑大灾难
● 23、巨型克雷机　　　　● 58、黑色的幽灵
● 24、爆发超新星　　　　● 59、多媒体之年
● 25、王安的悲剧　　　　● 60、海量存储器
● 26、英特尔创业　　　　● 61、声音起爆器
● 27、芯片计算机　　　　● 62、真实的谎言
● 28、牛郎星升空　　　　● 63、神奇的虚拟
● 29、微软树大旗　　　　● 64、奔腾的时代
● 30、微电脑先锋　　　　● 65、走出死亡谷
● 31、游戏机溯源　　　　● 66、天价启动我
● 32、车库谱新曲　　　　● 67、电影魔术师
● 33、苹果的滋味　　　　● 68、伟大的转折
● 34、大象踢踏舞　　　　● 69、重建ＩＢＭ
● 35、跨进新纪元　　　　● 70、人机世纪战

Data Compression The Complete Reference 3rd Edition

2008-03-06 10:47:53

"A wonderful treasure chest of information; spanning a wide range of data compression methods, from simple test compression methods to the use of wavelets in image compression. It is unusual for a text on compression to cover the field so completely." ACM Computing Reviews

"Salomons book is the most complete and up-to-date reference on the subject. The style, rigorous yet easy to read, makes this book the preferred choice [and] the encyclopedic nature of the text makes it an obligatory acquisition by our library." Dr Martin Cohn, Brandeis University

Data compression is one of the most important tools in modern computing, and there has been tremendous progress in all areas of the field. This fourth edition of Data Compression provides an all-inclusive, thoroughly updated, and user-friendly reference for the many different types and methods of compression (especially audio compression, an area in which many new topics covered in this revised edition appear).

Among the important features of the book are a detailed and helpful taxonomy, a detailed description of the most common methods, and discussions on the use and comparative benefits of different methods. The books logical, clear and lively presentation is organized around the main branches of data compression.

组合逻辑电路习题课（视频）

2008-02-13 19:48:39

数字逻辑

二极管原理

2008-02-13 19:22:50

数字逻辑基础

Introduction to Quantum Computers. 量子计算机导论

2008-01-26 22:14:13

Introduction to Quantum Computers

Author: Gary D. Doolen, Ronnie Mainieri, Vldimir I. Tsifrinovich
Publisher: World Scientific Publishing Company
Number Of Pages: 187
Publication Date: 1998-06
Sales Rank: 1966476
ISBN / ASIN: 9810234902
EAN: 9789810234904
Binding: Hardcover
Manufacturer: World Scientific Publishing Company
Studio: World Scientific Publishing Company
Average Rating: 5

Quantum computing promises to solve problems which are intractable on digital computers. Highly parallel quantum algorithms can decrease the computational time for some problems by many orders of magnitude. This important book explains how quantum computers can do these amazing things. Several algorithms are illustrated: the discrete Fourier transform, Shor's algorithm for prime factorization; algorithms for quantum logic gates; physical implementations of quantum logic gates in ion traps and in spin chains; the simplest schemes for quantum error correction; correction of errors caused by imperfect resonant pulses; correction of errors caused by the nonresonant actions of a pulse; and numerical simulations of dynamical behavior of the quantum Control-Not gate. An overview of some basic elements of computer science is presented, including the Turing machine, Boolean algebra, and logic gates. The required quantum ideas are explained.

电磁场与电磁波（电子教案）（通信类）

2008-01-10 02:45:22

第一章矢量分析
第二章宏观电磁现象的基本规律
第三章静电场及其边值问题的解法
第四章恒定电场与恒定磁场
第五章电磁波的辐射（以“只读”方式打开即可）
第六章均匀平面波的传播（同上）
第七章均匀波导中的导行电磁波
第八章均匀传输线中的导行电磁波
各章部分别习题解答
各章要点归纳

通信原理概论课件

2008-01-10 01:36:48

第一章通信概论
第二章典型通信系统概论
第三章模拟调制系统
第四章通信中的调制技术
第五章模拟信号编码技术
第六章数字基带传输系统
樊昌信版部分习题答案
曹志刚版电子教材
专题知识：数字光纤系统、数字移动通信系统、信号分析与信息论
复习一、二、三以及Flashexample

Optimal Stopping and Free Boundary Problems(最优停止与自由边界问题)

2007-12-27 10:32:12

The book aims at disclosing a fascinating connection between optimal stopping problems in probability and free-boundary problems in analysis using minimal tools and focusing on key examples.

The general theory of optimal stopping is exposed at the level of basic principles in both discrete and continuous time covering martingale and Markovian methods. Methods of solution explained range from classic ones (such as change of time, change of space, change of measure) to more recent ones (such as local time-space calculus and nonlinear integral equations).

A detailed chapter on stochastic processes is included making the material more accessible to a wider cross-disciplinary audience. The book may be viewed as an ideal compendium for an interested reader who wishes to master stochastic calculus via fundamental examples.

Areas of application where examples are worked out in full detail include financial mathematics, financial engineering, mathematical statistics, and stochastic analysis.

Visual Prolog 语言教程

2007-12-22 23:16:07

本书全面系统介绍Visual Prolog语言及其编程。全书共分四个部分，第一部分简短介绍Visual Prolog可视化开发环境；第二部分包括教程的第2章至第11章，教你如何学会用Visual Prolog编程；第三部分包括第12章至第16章，详细叙述Visual Prolog的预定义特性；第四部分包括第17章至第18章，完整而系统地叙述语言元素和模块化程序设计，以及与其它语言的接口。

下面是本书每一章的内容简介。

第一部分 Visual Prolog概述

第1章 Visual Prolog开发环境描述如何将Visual Prolog安装到你的计算机上，如何使用Visual Prolog的可视化开发环境来运行本书所提供的例子，提供一个快速指南，包括创建、运行及保存你第一个Visual Prolog程序的一些步骤，解释如何应用可视化开发环境的Test Goal实用程序来运行语言教程提供的一些Visual Prolog程序的例子。

第二部分学习Visual Prolog

第2章 Prolog基本原理从自然语言的观点对Prolog提供一个概括地介绍，讨论如何把自然语言的语句和问题转换为Prolog的事实、规则和询问。

第3章 Visual Prolog程序结构包括Visual Prolog的语法，Visual Prolog的程序段，用Visual Prolog进行编程。

第4章合一与回溯描述Visual Prolog如何求解问题，如何给变量赋值。

第5章简单对象与复合对象讨论声明和建立Visual Prolog中的结构。

第6章重复与递归解释如何应用回溯和递归编写重复性过程；还介绍了递归结构和树。

第7章表与递归介绍表及其递归用法，以及一般的表操作。

第8章内部事实数据库讨论使用Visual Prolog的事实段在运行时间对你的程序增加事实及存储全局信息。

第9章算术与比较运算介绍Visual Prolog内建的全部算术函数和比较函数，而且举例说明这些函数如何使用。

第10章高级技术控制流程分析，使用引用变量、谓词指针、二进制论域、项的转换，使用动态截断、工具及技术进行错误和信号处理，以及有效程序的编程风格。

第11章类和对象对面向对象程序设计进行了简要介绍，并介绍了Visual Prolog中的对象机制。

第三部分使用Visual Prolog

第12章文件操作谓词介绍Visual Prolog中的 I/O，包括读、写、文件和目录处理。

第13章字符串处理谓词包括各种串操作，如串比较、串变换、串构造和串分析。

第14章外部数据库系统包括Visual Prolog的外部数据库系统：链接数据、B+树、存储数据和分类数据，还包括构造实际数据库应用程序的一些例子。

第15章系统级编程介绍Visual Prolog内部支持的低级控件：系统调用、BIOS、低级存储器寻址和位操作。

第16章 Prolog程序举例提供一组花样繁多的Prolog程序，演示Prolog求解复杂问题的一些精巧的方法。

第四部分程序员指南

第17章语言元素给出Visual Prolog语言中所有特性的一个系统的综述。本章还介绍了模块化程序设计。

第18章与其它语言的接口给出如何与C语言和其它语言进行接口的一个描述。

微型计算机硬件组成

2007-12-20 11:06:12

目录
序言
前言
第1章微型计算机的基本知识 1
1.1 微型计算机系统概述 1
1.2 微型计算机系统的三个层次 3
1.3 微型计算机的分类 4
1.4 微型计算机系统的主要性能指标 4
1.5 微型计算机系统硬件结构 5
1.5.1 结构特点与框图 5
1.5.2 主要组成部分结构及其功能 6
1.5.3 输入输出（I/O）设备的接口 8
1.5.4 总线 9
1.6 微型计算机基本工作原理 9
1.6.1 指令与程序概述 9
1.6.2 指令与程序的执行 10
1.7 高档微型计算机中应用的现代先进
计算机技术 10
1.7.1 微程序控制技术 11
1.7.2 流水线技术 11
1.7.3 高速缓冲存储器技术 11
1.7.4 虚拟存储器技术 12
1.7.5 乱序执行技术 12
第2章组成微型计算机的主要部件 14
2.1 概述 14
2.2 微型计算机主要的组成部件 14
2.2.1 主板 14
2.2.2 机箱和电源 15
2.2.3 显示器 16
2.2.4 磁盘驱动器 16
2.2.5 键盘 17
2.2.6 各种适配电路卡 17
2.3 主板 18
2.3.1 主板的构架 18
2.3.2 芯片组 19
2.3.3 系统总线 20
2.3.4 局部总线 22
2.3.5 IDE（EIDE）接口 23
2.3.6 串行、并行通信接口 24
2.3.7 键盘、鼠标接口 25
2.3.8 USB通用串行总线 25
2.3.9 制约主板性能的一些因素 27
2.3.10 常见主板简介 27
2.4 中央处理器 28
2.4.1 微处理器概述 28
2.4.2 Intel 系列微处理器简介 29
2.4.3 AMD系列微处理器简介 32
2.4.4 其他公司的微处理器系列 33
2.5 内部存储器 35
2.5.1 内部存储器的基本概念 35
2.5.2 半导体存储器 35
2.5.3 现代微机使用的内存条 37
2.6 显示卡与显示器 38
2.6.1 微型计算机显示系统概述 38
2.6.2 显示适配器—显示卡 39
2.6.3 显示卡的分类 39
2.6.4 典型显示卡介绍 41
2.6.5 CRT显示器的性能指标 42
2.6.6 典型CRT显示器介绍 44
2.7 多功能接口 45
2.7.1 接口技术概论 45
2.7.2 微机中常见接口及其性能 46
2.7.3 多功能接口卡 49
2.8 硬盘与软盘 51
2.8.1 计算机磁盘存储系统概述 51
2.8.2 磁盘存储的主要技术指标 51
2.8.3 磁盘存储器的接口标准 52
2.8.4 软盘 54
2.8.5 硬盘 55
2.9 光盘存储器 56
2.9.1 CD-ROM光盘 56
2.9.2 CD-ROM驱动器 57
2.9.3 DVD光盘 58
2.10 键盘 62
2.11 鼠标器 64
2.12 声卡 64
第3章多媒体计算机 67
3.1 概述 67
3.2 多媒体的基本概念 67
3.3 多媒体计算机的关键技术及标准 68
3.3.1 多媒体数据的数字化技术 68
3.3.2 多媒体数据压缩及编码技术 68
3.3.3 多媒体硬件技术 69
3.3.4 虚拟现实 70
3.3.5 多媒体计算机的标准 70
3.4 关于音频的技术 72
3.4.1 声音的数字化 72
3.4.2 乐器数字接口（MIDI） 74
3.4.3 MP3 75
3.5 关于视频的技术 75
3.5.1 计算机图像与电视图像 76
3.5.2 常见的视频文件格式 76
3.5.3 常见的图形图像处理软件 79
3.5.4 VCD 播放软件 82
3.6 WINDOWS 98中的多媒体技术及
应用程序 83
3.6.1 Windows98中的多媒体技术 84
3.6.2 Windows98中的多媒体应用程序 84
3.7 常见的多媒体硬件 98
3.7.1 扫描仪 99
3.7.2 MIDI设备 102
3.7.3 触摸屏 102
3.7.4 数字相机 104
3.7.5 数字摄像机 105
3.7.6 顶置型摄像机 105
3.7.7 视频捕获卡 105
3.7.8 电视卡和电影卡 108
3.7.9 DVD 108
第4章笔记本计算机 111
4.1 笔记本计算机简介 111
4.2 LCD显示器 111
4.2.1 液晶显示器的基本原理 112
4.2.2 液晶显示器的主要性能指标 113
4.3 笔记本计算机的主要硬件 113
4.3.1 笔记本计算机的主板 113
4.3.2 笔记本计算机的CPU与Cache 114
4.3.3 笔记本计算机的内存 114
4.3.4 笔记本计算机的硬盘 114
4.3.5 笔记本计算机的软盘驱动器 115
4.3.6 笔记本计算机的光驱 116
4.3.7 笔记本计算机的键盘 116
4.3.8 笔记本计算机的鼠标 116
4.3.9 笔记本计算机的电池和AC电源
适配器 117
4.4 笔记本计算机的接口 118
4.4.1 COM串口 118
4.4.2 LPT并口 118
4.4.3 PS／2接口 118
4.4.4 外接显示器接口 118
4.4.5 USB接口 119
4.4.6 PCMCIA卡插口 119
4.4.7 红外通信口 120
4.4.8 电话线连接插口 120
4.4.9 音频系统插孔 120
4.4.10 扩展坞接口 120
4.5 笔记本计算机的发展趋势 121
4.6 笔记本计算机的使用与维护 122
4.7 笔记本计算机的应用 123
第5章计算机网络 125
5.1 概述 125
5.2 计算机网络的基本概念 125
5.2.1 计算机网络的分类 125
5.2.2 计算机网络协议 126
5.2.3 计算机网络的拓扑结构 127
5.2.4 计算机网络的工作模式 130
5.3 计算机网络的组网硬件 131
5.4 Internet 140
5.5 计算机网络的未来 141
5.5.1 Internet/Intranet/Extranet 142
5.5.2 IP电话 143
5.5.3 NC/NetPC 143
5.5.4 智能大厦 144
5.6 Windows98中的网络技术 146
5.6.1 Windows 98网络特性 146
5.6.2 Windows 98与LAN 147
5.6.3 Windows 98与Internet 155
第6章其它微型计算机常用外部设备 161
6.1 针式打印机 161
6.2 喷墨式打印机 162
6.3 激光打印机 163
6.4 绘图机 166
6.5 图形扫描仪 167
6.5.1 扫描仪的工作原理 167
6.5.2 扫描仪的性能 168
6.5.3 扫描仪的应用 169
6.6 光盘刻录机 170
第7章微型计算机DIY 172
7.1 概述 172
7.2 微型计算机的选型 172
7.3 微型计算机的组装 174
7.3.1 微型计算机的硬件安装 174
7.3.2 微型计算机的软件安装 180
7.4 BIOS的设置 180
7.4.1 CMOS设置和BIOS设置 180
7.4.2 何时进行BIOS设置 181
7.4.3 如何进入BIOS设置程序 181
7.4.4 进行BIOS设置 181
7.5 微型计算机性能的测试 182
7.5.1 WinBench 99 183
7.5.2 WinTune 98 183
第8章微型计算机的维护 185
8.1 概述 185
8.2 软故障的判断及其排除 185
8.2.1 计算机软故障概念及概述 185
8.2.2 常见软故障的种类 186
8.2.3 简单软故障的排除 186
8.3 硬故障的判断及其排除方法 187
8.3.1 常见硬故障的分类 187
8.3.2 系统故障常用检测及排除方法 188
8.3.3 其它外设故障及其排除方法 190
8.4 计算机病毒概述 190
附录A 主板上标准64字节的CMOS
RAM地址及其功能 193
附录B 主板???口引脚 196
附录C Intel X86系列CPU比较 198
附录D 多媒体技术对照词典 199
附录E 制订、颁布数据通信和计算机网
络标准的标准化组织 204
附录F 常见数据通信及计算机网络
标准 205

序言

经过10 余年的教学实践，层次教学已成为高等院校计算机基础教学的基本模式。教育部倡导的“计算机文化基础—计算机技术基础—计算机应用基础”3个层次，为高校实施计算机基础教学提供了一个科学的框架。

根据我们的理解，三层次教学中的文化基础为“入门”课，用于引导学生认识计算机文化在信息社会中的地位作用；技术基础为“拓宽”课，用于从硬件和软件两个方面扩充学生的知识和技能；它们构成三层次教学的“基础与核心”。而应用基础层则是三层次教学的“归宿”，其目的在于结合专业的需要“深化”在某一方面（或领域）的计算机应用知识，加强学生解决本专业计算机应用的能力。本系列教材就是按照这样的思路编写的。第一批书目包括一、二层次的6本计算机公共课教材，即：

《计算机文化引论》

《C 语言程序设计》

《计算机软件环境与工具》

《微机数据库应用》

《微型计算机硬件组成》

《计算机网络应用基础》

以后将陆续编写出版第三层次的教材，例如《微机原理与应用》、《图形处理与CAD基础》、《面向对象程序设计》等，以满足不同专业的学生深入学习的需要。

本系列教材具有下列特点：

一、体现了全新的课程体系。考虑到软件技术的发展，本系列在《计算机文化引论》课之后，用程序设计、数据库和软件环境与工具等3 本教材代替传统的一本教材—程序设计。又鉴于多媒体应用与网络应用在近10 年来发展迅猛，本系列在首批书目中列入了《微型计算机硬件组成》与《计算机网络应用基础》两种教材，分别介绍这两个方面的知识。这一课程体系既在总体上满足教育部三层次教学的内容，也突出了计算机基础教学重在应用、立足于提高学生素质、帮助学生建立强烈的计算机文化意识，提高计算机文化素质的需要。

二、按照知识单元安排每本教材的内容，自1994 年起编者就在教育部高校工科计算机基础课程教学指导委员会的支持下，开展对计算机基础课知识结构的研究。1996 年，该项研究被教育部列为面向21 世纪计算机基础教学项目组立项课题。上述6种教材，每种覆盖知识单元的一个领域，构成一个相对独立的教学模块，特别方便不同层次的高校与读者按需选用。

三、遴选作者，强强联手。参加编写本系列第一批教材的作者，都是根据本人的特长由所在学校推荐的、对该方面的教学和科研富有经验的教师。编写大纲统一由系列教材编辑委员会审定，对保证教材质量起到良好的作用。

本系列的出版得到四川省高校计算机基础教育研究会和机械工业出版社华章公司的大力支持。教育部计算机基础课程教学指导委员会委员、电子科技大学古天祥教授和教育部计算机基础课程教学指导委员会委员、四川大学李志蜀教授担任系列教材编辑委员会的顾问，对系列的选题与内容都提出宝贵的意见。借此机会，编者对他们表示诚挚的感谢。

史济民李光琳

2000 年6 月

前言

前言

本书不同于以往的同类型教科书。过去的同类型教科书几乎全是讲述计算机的原理、电路等比较抽象的东西。同学们学完后，无法同实际生活、实际应用的计算机硬件结合起来。在看到CPU、内存条时，分不清它们是什么；学了总线，不知道当前的各种微机采用的是什么总线，有什么类型的总线，看到计算机的电源线，会问老师这是不是总线……

本书也不同于一般的计算机报刊、杂志等科普性书籍。一般的计算机报刊、杂志等科普性书籍常常是简单地介绍各种计算机的常识，让读者掌握一大堆名词，而不知它们的工作原理、不知它们在系统中所处的位置和如何同系统的其他部件进行协调工作，结果是让读者成为一个“华而不实”的人。我们在编写本书时，一直在努力避免出现这种情况。

本书综合了上述两类书籍的优点，把两类知识有机地结合在一起。在讲原理的同时，讲述原理在实际生活中的应用、讲述原理在实际产品中是如何得以体现；介绍最新的技术、产品时，同时介绍这种技术、产品的原理和其在系统中所处的位置及其如何同其他部件协调工作，并与以往的老技术、老产品进行对比，让同学们对这种新技术、新产品有更深的认识，能更好地掌握这种新技术、新产品。

书中的习题，大多是用于开拓同学们思维的题。如：学习了第2章后，让同学们利用对计算机系统和CPU的发展史及其特点的了解，大胆预测10年后，微型计算机的CPU是“什么模样”；学习了第5章后，让同学们针对一个具体的网络工程，写一份关于网络工程的投标书。

本书是针对大学本、专科学生编写的新教材，但由于本书编写时采用了新的知识结构，也可以作为中专、中学的计算机教材。同时，由于本书也是极具应用性的书籍，所以同样适用于从事IT行业的人员和电脑爱好者。

在本书的知识结构中，重点在第1章、第2章和第3章。

本书由西南财经大学蔡学望教授主审，在此，感谢蔡老师提供了很好的意见。

作者

2000.2

Parsing Techniques a Practical Guide

2007-12-17 11:49:57

解析（语法分析）是计算机科学应用最广泛的分支之一，解析器在编译，数据库接口，自述数据，人工智能等方面有极广阔的应用，还有语言学（文本分析，主题分析，机器翻译），文档处理，生物、化学公式排版等也需要用到解析技术，但目前这方面的书籍还不多，也许是解析技术确实有点难吧。
是英文的电子书：
Preface 11
1 Introduction 13
2 Grammars as a generating device 16
3 Introduction to parsing 62
4 General non-directional methods 81
5 Regular grammars and finite-state automata 106
6 General directional top-down methods 119
7 General bottom-up parsing 144
8 Deterministic top-down methods 164
9 Deterministic bottom-up parsing 184
10 Error handling 229
11 Comparative survey 249
12 A simple general context-free parser 253
13 Annotated bibliography 264
Author index 313
Index 317

Introduction to Game Programming with C++

2007-12-14 12:08:40

Introduction to Game Programming with C++ explores the world of game development with a focus on C++. This book begins with an explanation of the basics of mathematics as it relates to game programming, covers the fundamentals of C++, and describes a number of algorithms commonly used in games. In addition, it discusses several libraries that can help you manage graphics, add audio, and create installation software so you can get started on the path to making both 2D and 3D games.

数字信号处理教程习题分析与解答.随书光盘

2007-12-13 13:28:50

人工智能基础

2007-12-12 11:39:45

本教材为中国计算机学会教育专业委员会和全国高等学校计算机教育研究会组织编写推荐出版的《计算机学科教育计划1993》的配套教材之一，并被纳入电子工业部《1996-2000年全国电子信息类专业教材编审出版规划》的规划教材。参考学时为50-70学时，内容涉及人工智能概况、知识表示、问题求解、机器学习、知识发现等机理，并介绍了人工智能发展的重要进展，等等。可作为本科生教材，也可作为研究生和在职专业人员学习教材。

Computer systems : a programmer's perspective 深入理解计算机系统（英文原版）

2007-12-08 19:23:35

★　AMAZON五星图书，最伟大计算机科学教材之一
★　卡耐基梅隆大学计算机学院院长，IEEE和ACM双院士倾力推出
★　超过80所美国和世界一流大学计算机专业选用本书为教材
看看指导教师们是如何评价这本书的：
“我坚信从程序员的角度来看计算机系统对教会学生计算机的内部结构非常有帮助。”——Kostas Daniilidis，宾夕法尼亚大学
“这本书讲述事物的方法与众不同，但是和我想要的课程进行方式类似。”——John Greiner，Rice大学
“这是一项出色的工作，是这一领域教学方法的一次革命。”——Michael Scott，罗切斯特大学
作者Randal E. Bryant是卡耐基梅隆大学的计算机科学系主任，ACM和IEEE双院士（Fellow），其研究成果多次获得ACM和IEEE颁发的大奖。
本书提供了大量的例子和练习及部分答案。尤其值得一提的是，对于每一个基本概念都有相应的笔头或程序试验，加深读者的理解。

Morgan.Kaufmann.Data.Mining.Practical.Machine.Learning.Tools.and.Techniques.Second.Edition.Jun.2005.

2007-12-06 00:18:04

The objective of this book is to introduce the tools and techniques for
machine learning that are used in data mining. After reading it, you will understand
what these techniques are and appreciate their strengths and applicability.
If you wish to experiment with your own data, you will be able to do this
easily with the Weka software.

微型计算机2007年第08期

2007-12-05 12:10:15

微型计算机2007年第08期

综合_计算机基础理论_计算机类_最新资料_得益网

《电脑常见故障处理》视频教程

超级容易学电脑——电脑故障恢复简易行

Wavelets in Medical Image Processing Denoising,Segmentation,and Registration

计算机系统设计与结构（第2版）

Advances in Nonlinear Signal and Image Processing (超清晰版)

电脑维护视频教程(非常实用的课程)

Hexagonal Image Processing

Compiler Construction - Lecture Notes in Computer Science

Advances in Applied Artificial Intelligence

模式识别和机器学习 Pattern Recognition and Machine Learning

并行计算 结构·算法·编程

Artificial Intelligence - A Modern Approach 2nd Edition

A Beautiful Math

计算机科学与技术方法论( 高清pdf完整版)

Multimedia Fingerprinting Forensics for Traitor Tracing

UWB Communication Systems-A Comprehensive Overview(超清晰板)

Smart Antennas-State of the Art

The Elements of Statistical Learning

Pattern Recognition and Machine Learning

Genomic Signal Processing and Statistics(高清晰板)

电脑史话（新版）

Data Compression The Complete Reference 3rd Edition

组合逻辑电路习题课（视频）

二极管原理

Introduction to Quantum Computers. 量子计算机导论

电磁场与电磁波（电子教案）（通信类）

通信原理概论课件

Optimal Stopping and Free Boundary Problems(最优停止与自由边界问题)

Visual Prolog 语言教程

微型计算机硬件组成

Parsing Techniques a Practical Guide

Introduction to Game Programming with C++

数字信号处理教程习题分析与解答.随书光盘

人工智能基础

Computer systems : a programmer's perspective 深入理解计算机系统（英文原版）

Morgan.Kaufmann.Data.Mining.Practical.Machine.Learning.Tools.and.Techniques.Second.Edition.Jun.2005.

微型计算机2007年第08期

并行计算结构·算法·编程