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本书综合考虑了有监督和无监督模式识别的经典的以及当前的理论和实践，为专业技术人员和高校学生建立起完整的基本知识体系。本书由模式识别领域的两位项级专家合著，从工程角度全面阐述了模式识别的应用，内容包括叶斯分类、贝叶斯网络、线性和非线性分类器（包含神经网络和支持向量机）、动态编程和用于顺序数据的隐马尔可夫模型、特征（包含小波、主成分分析、独立成分分析和分形分析）、特征选择技术、来自学习理论的基本概念、聚类概念和算法等。

本书是享誉世界的名著，内容既全面又相对独立，既有基础知识的介绍又有领域研究现状的介绍，还有对未来发展的展望，是本领域全面的参考书，被世界众多高校选用为教材。本书可作为高等院校计算机、电子、通信、自动化等专业研究生和高年级本科生的教材，也可作为计算机信息处理、自动控制等相关领域工程技术人员的参考用书。

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 Dwcision Surfaces

2.4 Bayesian Classification for Normal Distributions

2.5 Estimation of Unknown Probability Density Functions

2.6 The Nearest Neighbor Rule

2.7 Bayesian Networks

CHAPTER 3 LINEAR CLASSIFIERS

3.1 Introdutcion

3.2 linear Discriminant Functions and Decision Hyperplanes

3.3 The Percptron Algorithm

3.4 Least Squares Mwethods

3.5 Mean Square Estimation Revisited

3.6 Logistic Discrimination

3.7 Support Vector Machines

CHAPTER 4 IONLINEAR CLASSIFIERS

4.1 Introduction

4.2 The XOR Problem

4.3 The Two-Layer Perceptron

4.4 Three-Layer Perceptons

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 l-Dimensional Space in Linear Dichotomies

4.14 Polynomial Classifiers

4.15 Radial Basis Function Networks

4.16 Universal Approximatiors

4.17 Support Vector Machines: The Nonlinear Case

CHAPTER 5 FEATURE SELECTION

……

CHAPTER 6 FEATURE GENERATION Ⅰ：LINEAR TRANSFORMS

CHAPTER 7 FEATURE GENERATION Ⅱ

CHAPTER 8 TEMPLATE MATCHING

CHAPTER 9 CONTEXT-DEPENDENT CLASIFICATION

CHAPTER 10 SYSTEM EVALUATION

CHAPTER 11 CLUSTERING：BASIC CONCEPTS

CHAPTER 12 CLUSTERING ALGORITHMSⅠ：SEQUENTIAL ALGORITHMS

CHAPTER 13 CLUSTERING ALGORITHMSⅡ：HIERARCHICAL ALGORITHMS

CHAPTER 14 CLUSTERING ALGORITHMSⅢ：SCHEMES BASED ON FUNCTION OPTIMIZATION

CHAPTER 15 CLUSTERING ALGORITHMSⅣ

CHAPTER 16 CLUSTER VALIDITY

Appendix A Hints form Probability and Statistics

Appendix B Linear Algebra Basics

Sergios Theodoridis希腊雅典大学信息与通信系教授。他于1973年在雅典大学获得物理学学士学位，又分别于1975年和1978年在英国伯翰大学获信息处理与通信硕士和博士学位。他的主要研究方向是自适应信号处、通信与模式识别。他是欧洲并行结构及语言协会（PARLEE-95）的主席和欧

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A general principle adopted by most of these techniques is the decomposition of the problem into smaller problems that are easier to handle.

For each smaller problem, a single node is employed. Its parameters are determined either iteratively using appropriate learning algorithms.

From the way these algorithms build the network, they are sometimes referred to as constructive techniques.

The other direction we will follow to design a multilayer perceptron is to fix the architecture and compute its synaptic parameters so as to minimize an appropriate cost function of its output.

This method not only overcomes the drawback of the resulting large networks of the previous section but also makes these networks powerful tools for a number of other applications, beyond pattern recognition.

However, this method is confronted with a serious difficulty. This is discontinuity of the step(activation) function, prohibiting differentiation with respect to the unknown parameters(synaptic weights).

A popluar family of continuous differentiable functions, which approximate the step function, is the family of sigmoid functions. A typical representative is the logistic function:

f(x) = frac{1}{1+exp(-ax)}

All these functions are also known as squashing functions since their output is limited in a finite range of values.

From the moment we move away from the step function, all we have said before about mapping the input vectors onto the vertices of a unit hyper-cube is no longer valid. It is now the cost function that takes on the burden for correct classification.

书籍介绍

本书综合考虑了有监督和无监督模式识别的经典的以及当前的理论和实践，为专业技术人员和高校学生建立起完整的基本知识体系。本书由模式识别领域的两位项级专家合著，从工程角度全面阐述了模式识别的应用，内容包括叶斯分类、贝叶斯网络、线性和非线性分类器（包含神经网络和支持向量机）、动态编程和用于顺序数据的隐马尔可夫模型、特征（包含小波、主成分分析、独立成分分析和分形分析）、特征选择技术、来自学习理论的基本概念、聚类概念和算法等。

　　本书是享誉世界的名著，内容既全面又相对独立，既有基础知识的介绍又有领域研究现状的介绍，还有对未来发展的展望，是本领域最全面的参考书，被世界众多高校选用为教材。本书可作为高等院校计算机、电子、通信、自动化等专业研究生和高年级本科生的教材，也可作为计算机信息处理、自动控制等相关领域工程技术人员的参考用书。