模式识别(英文版·第2版) (2010 年度畅销榜NO.2622 )

模式识别在所有的自动化，信息处理和检索应用中都至关重要。本书由该领域内的两位顶级专家合著而成，从工 程角度，全面阐述了模式识别的应用，涉及的主题从图像分析到语音识别与通信，书中涉及到了神经网络的前沿材料，着重描述了包括独立分量和支持向量机在内的最新进展。本书是享誉世界的名著，经过十余年的发展，已成为此领域 最全面的参考书，被世界众多高校选用为教材。除了适合教学外，也可供工程技术人员参考。
本书的主要特点：
最新的特征生成技术，包括基于小波。小波包，分形的特征，还阐述了独立分量分析。
新增了关子支持向量机，变形模板匹配的章节，以及关于约束优化的附录。
特征选择技术。
线性以及非线性分类器的设计，包括贝叶斯分类器、多层感知器，决策树和RBF网络。
独立于上下文的分类，包括动态规划和隐马尔科夫建模技术。
不仅介绍了聚类算法的最新发展，而且还介绍了一些经典方法，诸如模糊。基因、退火等算法技术。
各种应用，包括图像分析。字符识别，医学诊断。语音识别以及信道均衡。

Preface

CHAPTER 1 INTRODUCTION
1.1 Is Pattern Recognition Important?
1.2 Features, Feature Vectors, and Classifiers
1.3 Supervised Versus Unsupervised Pattern Recognition
1.4 Outline of the Book

CHAPTER 2 CLASSIFIERS BASED ON BAYES DECISION THEORY
2.1 Introduction
2.2 Bayes Decision Theory
2.3 Discriminant Functions and Decision Surfaces
2.4 Bayesian Classification for Normal Distributions
2.5 Estimation of Unknown Probability Density Functions
2.6 The Nearest Neighbor Rule

CHAPTER 3 LINEAR CLASSIFIERS
3.1 Introduction
3.2 Linear Discriminant Functions and Decision Hyperplanes
3.3 The Perceptron Algorithm
3.4 Least Squares Methods
3.5 Mean Square Estimation Revisited
3.6 Support Vector Machines

CHAPTER 4 NONLINEAR CLASSIFIERS
4.1 Introduction
4.2 The XOR Problem
4.3 The Two-Layer Perceptron
4.4 Three-Layer Perceptrons
4.5 Algorithms Based on Exact Classification of the Training Set
4.6 The Backpropagation Algorithm
4.7 Variations on the; Backpropagation Theme
4.8 The Cost Function Choice
4.9 Choice of the Network Size
4.10 A Simulation Example
4.11 Networks With Weight Sharing
4.12 Generalized Linear Classifiers
4.13 Capacity of the/-Dimensional Space in Linear Dichotomies
4.14 Polynomial Classifiers
4.15 Radial Basis Function Networks
4.16 Universal Approximators
4.17 Support Vector Machines: The Nonlinear Case
4.18 Decision Trees
4.19 Discussion

CHAPTER 5 FEATURE SELECTION
5.1 Introduction
5.2 Preprocessing
5.3 Feature Selection Based on Statistical Hypothesis Testing
5.4 The Receiver Operating Characteristics CROC Curve
5.5 Class Separability Measures
5.6 Feature Subset Selection
5.7 Optimal Feature Generation
5.8 Neural Networks and Feature Generation/Selection
5.9 A Hint on the Vapnik--Chemovenkis Learning Theory

CHAPTER 6 FEATURE GENERATION I: LINEAR TRANSFORMS
6.1 Introduction
6.2 Basis Vectors and Images
6.3 The Karhunen-Loeve Transform
6.4 The Singular Value Decomposition
6.5 Independent Component Analysis
6.6 The Discrete Fourier Transform (DFT)
6.7 The Discrete Cosine and Sine Transforms
6.8 The Hadamard Transform
6.9 The Haar Transform
6.10 The Haar Expansion Revisited
6.11 Discrete Time Wavelet Transform (DTWT)
6.12 The Multiresolution Interpretation
6.13 Wavelet Packets
6.14 A Look at Two-Dimensional Generalizations
6.15 Applications

CHAPTER 7 FEATURE GENERATION II
7.1 Introduction
7.2 Regional Features
7.3 Features for Shape and Size Characterization
7.4 A Glimpse at Fractals

CHAPTER 8 TEMPLATE MATCHING
8.1 Introduction
8.2 Measures Based on Optimal Path Searching Techniques
8.3 Measures Based on Correlations
8.4 Deformable Template Models

CHAPTER 9 CONTEXT-DEPENDENT CLASSIFICATION
9.1 Introduction
9.2 The Bayes Classifier
9.3 Markov Chain Models
9.4 The Viterbi Algorithm
9.5 Channel Equalization
9.6 Hidden Markov Models
9.7 Training Markov Models via Neural Networks
9.8 A discussion of Markov Random Fields

CHAPTSR 10 SYSTEM EVALUATION
10.1 Introduction
10.2 Error Counting Approach
10.3 Exploiting the Finite Size of the Data Set
10.4 A Case Study From Medical Imaging

CHAPTER 11 CLUSTERING: BASIC CONCEPTS
11.1 Introduction
11.2 Proximity Measures

CHAPTER 12 CLUSTERING ALGORITHMS I: SEQUENTIAL ALGORITHMS
12.1 Introduction
12.2 Categories of Clustering Algorithms
12.3 Sequential Clustering Algorithms
12.4 A Modification of BSAS
12.5 A Two-Threshold Sequential Scheme
12.6 Refinement Stages
12.7 Neural Network Implementation

CHAPTER 13 CLUSTERING ALGORITHMS II: HIERARCHICAL ALGORITHMS
13.1 Introduction
13.2 Agglomerative Algorithms
13.3 The Cophenetic Matrix
13.4 Divisive Algorithms
13.5 Choice of the Best Number of Clusters

CHAPTER 14 CLUSTERING ALGORITHMS III: SCHEMES BASED ON FUNCTION OPTIMIZATION
14.1 Introduction
14.2 Mixture Decomposition Schemes
14.3 Fuzzy Clustering Algorithms
14.4 Possibilistic Clustering
14.5 Hard Clustering Algorithms
14.6 Vector Quantization

CHAPTER 15 CLUSTERING ALGORITHMS IV
15.1 Introduction
15.2 Clustering Algorithms Based on Graph Theory
15.3 Competitive Learning Algorithms
15.4 Branch and Bound Clustering Algorithms
15.5 Binary Morphology Clustering Algorithms (BMCAs)
15.6 Boundary Detection Algorithms
15.7 Valley-Seeking Clustering Algorithms
15.8 Clustering Via Cost Optimization (Revisited)
15.9 Clustering Using Genetic Algorithms
15.10 Other Clustering Algorithms

CHAPTER 16 CLUSTER VALIDITY
16.1 Introduction
16.2 Hypothesis Testing Revisited
16.3 Hypothesis Testing in Cluster Validity
16.4 Relative Criteria
16.5 Validity of Individual Clusters
16.6 Clustering Tendency

Appendix A
Hints from Probability and Statistics

Appendix B
Linear Algebra Basics

Appendix C
Cost Function Optimization

Appendix D
Basic Definitions from Linear Systems Theory

Index

This book is the outgrowth of our teaching advanced undergraduate and gradu-ate courses over the past 20 years. These courses have been taught to differentaudiences, including students in electrical and electronics engineering, computer engineering, computer science and informatics, as well as to an interdisciplinary audience of a graduate course on automation. This experience led us to make the book as self-contained as possible and to address students with different back-grounds. As prerequisitive knowledge the reader requires only basic calculus,elementary linear algebra, and some probability theory basics. A number of mathe-matical tools, such as probability and statistics as well as constrained optimization,needed by various chapters, are treated in four Appendices. The book is designed to serve as a text for advanced undergraduate and graduate students, and it can be used for either a one- or a two-semester course. Furthermore, it is intended to be used as a self-study and reference book for research and for the practicing scientist/engineer. This latter audience was also our second incentive for writing this book, due to the involvement of our group in a number of projects related to pattern recognition. The philosophy of the book is to present various pattern recognition tasks in a unified way, including image analysis, speech processing, and communication applications. Despite their differences, these areas do share common features and their study can only benefit from a unified approach. Each chapter of the book starts with the basics and moves progressively to more advanced topics and reviews up-to-date techniques. A number of problems and computer exercises are given at the end of each chapter and a solutions manual is available from the publisher. Furthermore, a number of demonstrations based on MATLAB are available via the web at the book's site, http://www, di.uoa.gr/~stpatrec. Our intention is to update the site regularly with more and/or improved versions of these demonstrations. Suggestions are always welcome. Also at this web site, a page will be available for typos, which are unavoidable, despite frequent careful reading. The authors would appreciate readers notifying them about any typos found. This book would have not be written without the constant support and help from a number of colleagues and students throughout the years. We are especially indebted to Prof. K. Berberidis, Dr. E. Kofidis, Prof. A. Liavas, Dr. A. Rontogiannis, Dr. A. Pikrakis, Dr. Gezerlis and Dr. K. Georgoulakis. The constant support provided by Dr. I. Kopsinis from the early stages up to the final stage, with those long nights,has been invaluable. The book improved a great deal after the careful reading and the serious comments and suggestions of Prof. G. Moustakides, Prof. V. Digalakis,Prof. T. Adali, Prof. M. Zervakis, Prof. D. Cavouras, Prof. A. B0hm, Prof. G. Glentis, Prof. E. Koutsoupias, Prof. V. Zissimopoulos, Prof. A. Likas, Dr. A.Vassiliou, Dr. N. Vassilas, Dr. V. Drakopoulos, Dr. S. Hatzispyros. We are greatly indebted to these colleagues for their time and their constructive criticisms. Our collaboration and friendship with Prof. N. Kalouptsidis have been a source of constant inspiration for all these years. We are both deeply indebted to him. Last but not least, K. Koutroumbas would like to thank Sophia for her tolerance and support and S. Theodoridis would like to thank Despina, Eva, and Eleni, his joyful and supportive "harem."