模式识别(英文版·第3版)

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。

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

前言：

This book is the outgrowth of our teaching advanced undergraduate and graduate courses over the past 20 years. These courses have been taught to different audiences, 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 backgrounds. As prerequisitive knowledge the reader requires only basic ca..

我来评论这本书