附註:Includes bibliographical references and index.
Foreword; Preface; Contents; Chapter 1 PATTERN RECOGNITION: EVOLUTION OF METHODOLOGIES AND DATA MINING; 1.1 Introduction; 1.2 The pattern recognition problem; 1.3 The statistical approach; 1.4 The syntactic approach; 1.5 Classification trees; 1.6 The fuzzy set theoretic approach; 1.7 The connectionist approach; 1.8 Use of genetic algorithms; 1.9 The hybrid approach and soft computing; 1.10 Data mining and knowledge discovery; 1.11 Conclusions; Chapter 2 IMPERFECT SUPERVISION IN STATISTICAL PATTERN RECOGNITION; 2.1 Statistical pattern recognition; 2.2 Preliminaries; 2.3 Unsupervised learning
2.4 Models for imperfect supervision2.5 Effect of imperfect supervision; 2.6 Learning with an unreliable supervisor; 2.7 Learning with a stochastic supervisor; Chapter 3 ADAPTIVE STOCHASTIC ALGORITHMS FOR PATTERN CLASSIFICATION; 3.1 Introduction; 3.2 Learning automata; 3.3 A common payoff game of automata for pattern classification; 3.4 Three layer network consisting of teams of automata for pattern classification; 3.5 Modules of learning automata; 3.6 Discussion; Chapter 4 UNSUPERVISED CLASSIFICATION: SOME BAYESIAN APPROACHES; 4.1 Introduction
4.2 Finite mixtures of probability distributions4.3 Bayesian approaches for mixture decomposition; 4.4 Discussion; Chapter 5 SHAPE IN IMAGES; 5.1 High-level Bayesian image analysis; 5.2 Prior models for objects; 5.3 Inference; 5.4 Multiple objects and occlusions; 5.5 Warping and image averaging; 5.6 Discussion; Chapter 6 DECISION TREES FOR CLASSIFICATION : A REVIEW AND SOME NEW RESULTS; 6.1 Introduction; 6.2 The different node splitting criteria; 6.3 Pruning; 6.4 Look-ahead; 6.5 Other issues in decision tree construction; 6.6 A new look-ahead criterion: some new results; 6.7 Conclusions
Chapter 7 SYNTACTIC PATTERN RECOGNITION7.1 Introduction; 7.2 Primitive selection strategies; 7.3 Formal linguistic model: basic definitions and concepts; 7.4 High-dimensional pattern grammars; 7.5 Structural recognition of imprecise patterns; 7.6 Grammatical inference; 7.7 Recognition of ill-formed patterns: error-correcting grammars; Chapter 8 FUZZY SETS AS A LOGIC CANVAS FOR PATTERN RECOGNITION; 8.1 Introduction: fuzzy sets and pattern recognition; 8.2 Fuzzy set-based transparent topologies of the pattern classifier; 8.3 Supervised, unsupervised, and hybrid modes of learning
8.4 ConclusionsChapter 9 FUZZY PATTERN RECOGNITION BY FUZZY INTEGRALS AND FUZZY RULES; 9.1 Introduction; 9.2 Classification by fuzzy rules; 9.3 Classification by fuzzy integrals; Chapter 10 NEURAL NETWORK BASED PATTERN RECOGNITION; 10.1 Introduction; 10.2 The essence of pattern recognition; 10.3 Advanced neural network architectures; 10.4 Neural pattern recognition; 10.5 Conclusions; Chapter 11 PATTERN CLASSIFICATION BASED ON QUANTUM NEURAL NETWORKS: A CASE STUDY; 11.1 Introduction; 11.2 Quantum neural networks; 11.3 Wind profilers; 11.4 Formulation of the bird removal problem
摘要:This volume, containing contributions by experts from all over the world, is a collection of 21 articles which present review and research material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, syntactic/linguistic, fuzzy-set-theoretic, neural, genetic-algorithmic and rough-set-theoretic to hybrid soft computing, with significant real-life applications. In addition, the book describes efficient soft machine learning algorithms for data mining and knowledge discovery. With a balanced mixture of theory, algorithms and applications, as well as up-to-date information and an extensive bibliography, "Pattern Recognition: From Classical to Modern Approaches" is a useful resource.
