Pattern recognition : from classical to modern approaches

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Pattern recognition : from classical to modern approaches / editors, Sankar K. Pal, Amita Pal

資料種別:

図書

出版情報:

Singapore : World Scientific, c2001

形態:

xxii, 612 p. ; 23 cm

著者名:

ISBN:

9789810246846 [9810246846]

書誌ID:

BA58418692

子書誌情報

フルテキスト

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書誌詳細

注記:

Includes bibliographical references and index

主題:

情報理論

言語:

英語

目次情報:

Foreword

Preface

Pattern Recognition: Evolution of Methodologies and Data Mining / A. Pal ; S. K. PalChapter 1：

Introduction / 1.1：

The pattern recognition problem / 1.2：

The statistical approach / 1.3：

The syntactic approach / 1.4：

Classification trees / 1.5：

The fuzzy set theoretic approach / 1.6：

The connectionist approach / 1.7：

Use of genetic algorithms / 1.8：

The hybrid approach and soft computing / 1.9：

Data mining and knowledge discovery / 1.10：

Conclusions / 1.11：

Imperfect Supervision in Statistical Pattern Recognition / T. KrishnanChapter 2：

Statistical pattern recognition / 2.1：

Preliminaries / 2.2：

Unsupervised learning / 2.3：

Models for imperfect supervision / 2.4：

Effect of imperfect supervision / 2.5：

Learning with an unreliable supervisor / 2.6：

Learning with a stochastic supervisor / 2.7：

Adaptive Stochastic Algorithms for Pattern Classification / M. A. L. Thathachar ; P. S. SastryChapter 3：

Learning automata / 3.1：

A common payoff game of automata for pattern classification / 3.3：

Three layer network consisting of teams of automata for pattern classification / 3.4：

Modules of learning automata / 3.5：

Discussion / 3.6：

Unsupervised Classification: Some Bayesian Approaches / Chapter 4：

Finite mixtures of probability distributions / 4.1：

Bayesian approaches for mixture decomposition / 4.3：

Shape In Images / K. V. Mardia4.4：

High-level Bayesian image analysis / 5.1：

Prior models for objects / 5.2：

Inference / 5.3：

Multiple objects and occlusions / 5.4：

Warping and image averaging / 5.5：

Decision Trees for Classification: A Review and Some New Results / R. Kothari ; M. Dong5.6：

The different node splitting criteria / 6.1：

Pruning / 6.3：

Look-ahead / 6.4：

Other issues in decision tree construction / 6.5：

A new look-ahead criterion: some new results / 6.6：

Syntactic Pattern Recognition / A. K. Majumdar ; A. K. Ray6.7：

Primitive selection strategies / 7.1：

Formal linguistic model: basic definitions and concepts / 7.3：

High-dimensional pattern grammars / 7.4：

Structural recognition of imprecise patterns / 7.5：

Grammatical inference / 7.6：

Recognition of ill-formed patterns: error-correcting grammars / 7.7：

Fuzzy Sets as A Logic Canvas for Pattern Recognition / W. Pedrycz ; N. PizziChapter 8：

Introduction: fuzzy sets and pattern recognition / 8.1：

Fuzzy set-based transparent topologies of the pattern classifier / 8.2：

Supervised, unsupervised, and hybrid modes of learning / 8.3：

Fuzzy Pattern Recognition by Fuzzy Integrals and Fuzzy Rules / M. Grabisch8.4：

Classification by fuzzy rules / 9.1：

Classification by fuzzy integrals / 9.3：

Neural Network Based Pattern Recognition / V. David Sanchez A.Chapter 10：

The essence of pattern recognition / 10.1：

Advanced neural network architectures / 10.3：

Neural pattern recognition / 10.4：

Pattern Classification Based on Quantum Neural Networks: A Case Study / N. B. Karayiannis ; R. Kretzschmar ; H. Richner10.5：

Quantum neural networks / 11.1：

Wind profilers / 11.3：

Formulation of the bird removal problem / 11.4：

Experimental results / 11.5：

Networks of Spiking Neurons in Data Mining / K. Cios ; D.M. Sala11.6：

Graph algorithms / 12.1：

Clustering / 12.3：

Critical path method / 12.4：

The longest common subsequence / 12.5：

Genetic Algorithms, Pattern Classification and Neural Networks Design / S. Bandyopadhyay ; C. A. Murthy12.6：

Overview of genetic algorithms / 13.1：

Description of the genetic classifiers / 13.3：

Determination of MLP architecture / 13.4：

Discussion and conclusions / 13.5：

Rough Sets in Pattern Recognition / A. Skowron ; R. SwiniarskiChapter 14：

Basic rough set approach / 14.1：

Searching for knowledge / 14.2：

Hybrid methods / 14.3：

Combining Classifiers: Soft Computing Solutions / L. I. Kuncheva14.4：

Classifier combination / 15.1：

Soft computing in classifier combination / 15.3：

Automated Generation of Qualitative Representations of Complex Objects by Hybrid Soft-Computing Methods / E. H. Ruspini ; I. S. Zwir15.4：

Problem / 16.1：

Approach / 16.3：

Neuro-Fuzzy Models for Feature Selection and Classification / R. K. De16.4：

A brief review / 17.1：

Neuro-fuzzy methods for feature selection / 17.3：

Neuro-fuzzy knowledge-based classification / 17.4：

Results / 17.5：

Conclusions and Discussion / 17.6：

Adaptive Segmentation Techniques for Hyperspectral Imagery / H. Kwon ; S. Z. Der ; N. M. NasrabadiChapter 18：

Hyperspectral imaging system / 18.1：

Segmentation of hyperspectral imagery / 18.3：

Adaptive segmentation based on iterative local feature extraction / 18.4：

Adaptive unsupervised segmentation / 18.5：

Pattern Recognition Issues in Speech Processing / B. Yegnanarayana ; C. Chandra Sekhar18.6：

Nature of speech signal / 19.1：

Feature extraction in speech / 19.3：

Pattern recognition models for speech recognition / 19.4：

Challenges in pattern recognition tasks in speech / 19.5：

Writing Speed and Writing Sequence Invariant On-Line Handwriting Recognition / S.-H. Cha ; S. N. SrihariChapter 20：

Writing speed invariance / 20.1：

Writing sequence invariance / 20.3：

Recognizer / 20.4：

Tongue Diagnosis Based on Biometric Pattern Recognition Technology / K. Wang ; D. Zhang ; N. Li ; B. Pang20.5：

Tongue image capturing / 21.1：

Segmentation of tongue images / 21.3：

Tongue feature extraction / 21.4：

Tongue classification / 21.5：

Index / 21.6：

About the editors

Foreword

Preface

Pattern Recognition: Evolution of Methodologies and Data Mining / A. Pal ; S. K. PalChapter 1：

Introduction / 1.1：

The pattern recognition problem / 1.2：

The statistical approach / 1.3：

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