模式識別

前言

This book is the outgrowth of our teaching advanced undergraduate and graduatecourses over the past 20 years.These courses have been taught to differentaudiences, including students in electrical and electronics engineering, computerengineering, computer science, and informatics, as well as to an interdisciplinaryaudience of a graduate course on automation. This experience led us to makethe 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 fourAppendices. The book is designed toserve as a text for advanced undergraduate and graduate students, and it can be usedfor either a one- or a two-semester course. Furthermore,it is intended to be used as aself-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 theinvolvement of our group in a number of projects related to pattern recognition.

內(nèi)容概要

本書是享譽世界的名著，內(nèi)容既全面又相對獨立，既有基礎知識的介紹，又有本領域研究現(xiàn)狀的介紹，還有對未來發(fā)展的展望，是本領域最全面的參考書，被世界眾多高校選用為教材。本書可作為高等院校計算機。電子、通信。自動化等專業(yè)研究生和高年級本科生的教材，也可作為計算機信息處理、自動控制等相關領域的工程技術人員的參考用書?！　”緯饕攸c　　提供了大型數(shù)據(jù)集和高維數(shù)據(jù)的聚類算法以及網(wǎng)絡挖掘和生物信息學應用的最新資料?！　『w了基于圖像分析、光學字符識別，信道均衡，語音識別和音頻分類的多種應用?！　〕尸F(xiàn)了解決分類和穩(wěn)健回歸問題的內(nèi)核方法取得的最新成果?！　〗榻B了帶有Boosting方法的分類器組合技術?！　√峁└嗵幚磉^的實例和圖例，加深讀者對各種方法的了解?！　≡黾恿岁P于熱點話題的新的章節(jié)，包括非線性維數(shù)約減、非負矩陣分解、實用性反饋。穩(wěn)健回歸、半監(jiān)督學習，譜聚類和聚類組合技術。

作者簡介

西奧多里德斯，希臘雅典大學信息系教授。主要研究方向是自適應信號處理、通信與模式識別。他是歐洲并行結(jié)構(gòu)及語言協(xié)會（PARLE-95）的主席和歐洲信號處理協(xié)會（EUSIPCO-98）的常務主席、《信號處理》雜志編委。

書籍目錄

Preface CHAPTER 1 Introduction 　1.1 Is Pattern Recognition Important? 　1.2 Features, Feature Vectors, and Classifiers 　1.3 Supervised, Unsupervised, and Semi-Supervised Learning 　1.4 MATLAB Programs 　1.5 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 　2.7 Bayesian Networks 　2.8 Problems 　References CHAPTER 3 Linear Classifiers 　3.1 Introduction  3.2 Linear Discriminant Functions and Decision  3.3 The Perceptron Algorithm   3.4 Least Squares Methods   3.5 Mean Square Estimation Revisited   3.6 Logistic Discrimination  3.7 Support Vector Machines  3.8 ProblesmCHAPTER 4 Nonlinear ClassifiersCHAPTER 5 Feature SelectionCHAPTER 6 Feature Generation I: Data Transformation and Dimensionality ReductionCHAPTER 7 Feature Generation IICHAPTER 8 Template MatchingCHAPTER 9 Context-Dependent ClassificationCHAPTER 10 Supervised Learning: The EpilogueCHAPTER 11 Clustering: Basic ConceptsCHAPTER 12 Clustering Algorithms I: Sequential AlgorithmsCHAPTER 13 Clustering Algorithms II: Hierarchical AlgorithmsCHAPTER 14 Clustering Algorithms III: Schemes Based on Function OptimizationCHAPTER 15 Clustering Algorithms IVCHAPTER 16 Cluster ValidityAPPENDIX A Hints from Probability and StatisticsAPPENDIX B Linear Algebra BasicsAPPENDIX C Cost Function OptimizationAPPENDIX D Basic Definitions from Linear Systems TheoryIndex

章節(jié)摘錄

插圖：Chapter 14 deals with clustering algorithms based on cost function optimization,using tools from differential calculus. Hard clustering and fuzzy and possibilisticschemes axe considered, based on various types of cluster representatives, includingpoint representatives, hyperplane representatives, and shell-shaped representatives.In a first course, most of these algorithms are bypassed, and emphasis is given tothe isodata algorithm.Chapter 15 features a high degree of modularity. It deals with clustering algo-rithms based on different ideas,which cannot be grouped under a single philosophy.Spectral clustering, competitive learning, branch and bound, simulated annealing,and genetic algorithms are some of the schemes treated in this chapter. These arebypassed in a first course.Chapter 16 deals with the clustering validity stage of a clustering procedure. Itcontains rather advanced concepts and is omitted in a first course. Emphasis is givento the definitions of internal, external, and relative criteria and the random hypothe-ses used in each case. Indices, adopted in the framework of external and internalcriteria, are presented, and examples are provided showing the use of these indices.Syntactic pattern recognftfon methods are not treated in this book. Syntacticpattern recognition methods differ in philosophy from the methods discussed inthis book and, in general, are applicable to different types of problems. In syntacticpattern recognition, the structure of the patterns is of paramount importance, andpattern recognition is performed on the basis of a set of pattern primitives, a setof rules in the form of a grammar, and a recognizer called automaton. Thus, wewere faced with a dilemma: either to increase the size of the book substantially, orto provide a short overview （which, however, exists in a number of other books）,or to omit it. The last option seemed to be the most sensible choice.

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