Pattern Recognition

From Classical To Modern Approaches

By: Sankar Kumar Pal (Editor), Amita Pal (Editor)

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Hardcover | 26 November 2001

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636 Pages

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21.59 x 15.88 x 3.81

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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 very useful resource.

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Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Computer Vision Pattern Recognition Image Processing Systems Analysis & Design Booktopia Publisher Services World Scientific Publishing Computer Programming & Software Development Graphical & Digital Media Applications

Foreword	p. vii
Preface	p. ix
Pattern Recognition: Evolution of Methodologies and Data Mining	p. 1
Introduction	p. 2
The pattern recognition problem	p. 3
The statistical approach	p. 5
The syntactic approach	p. 8
Classification trees	p. 9
The fuzzy set theoretic approach	p. 10
The connectionist approach	p. 12
Use of genetic algorithms	p. 14
The hybrid approach and soft computing	p. 15
Data mining and knowledge discovery	p. 16
Conclusions	p. 18
Imperfect Supervision in Statistical Pattern Recognition	p. 25
Statistical pattern recognition	p. 26
Preliminaries	p. 30
Unsupervised learning	p. 38
Models for imperfect supervision	p. 45
Effect of imperfect supervision	p. 49
Learning with an unreliable supervisor	p. 52
Learning with a stochastic supervisor	p. 55
Adaptive Stochastic Algorithms for Pattern Classification	p. 67
Introduction	p. 67
Learning automata	p. 75
A common payoff game of automata for pattern classification	p. 82
Three layer network consisting of teams of automata for pattern classification	p. 93
Modules of learning automata	p. 103
Discussion	p. 107
Unsupervised Classification: Some Bayesian Approaches	p. 115
Introduction	p. 115
Finite mixtures of probability distributions	p. 117
Bayesian approaches for mixture decomposition	p. 119
Discussion	p. 133
Shape In Images	p. 147
High-level Bayesian image analysis	p. 148
Prior models for objects	p. 149
Inference	p. 154
Multiple objects and occlusions	p. 157
Warping and image averaging	p. 159
Discussion	p. 161
Decision Trees for Classification: A Review and Some New Results	p. 169
Introduction	p. 169
The different node splitting criteria	p. 171
Pruning	p. 174
Look-ahead	p. 176
Other issues in decision tree construction	p. 176
A new look-ahead criterion: some new results	p. 177
Conclusions	p. 181
Syntactic Pattern Recognition	p. 185
Introduction	p. 186
Primitive selection strategies	p. 188
Formal linguistic model: basic definitions and concepts	p. 190
High-dimensional pattern grammars	p. 194
Structural recognition of imprecise patterns	p. 196
Grammatical inference	p. 203
Recognition of ill-formed patterns: error-correcting grammars	p. 221
Fuzzy Sets as A Logic Canvas for Pattern Recognition	p. 231
Introduction: fuzzy sets and pattern recognition	p. 232
Fuzzy set-based transparent topologies of the pattern classifier	p. 233
Supervised, unsupervised, and hybrid modes of learning	p. 245
Conclusions	p. 253
Fuzzy Pattern Recognition by Fuzzy Integrals and Fuzzy Rules	p. 257
Introduction	p. 257
Classification by fuzzy rules	p. 258
Classification by fuzzy integrals	p. 266
Neural Network Based Pattern Recognition	p. 281
Introduction	p. 281
The essence of pattern recognition	p. 282
Advanced neural network architectures	p. 285
Neural pattern recognition	p. 288
Conclusions	p. 295
Pattern Classification Based on Quantum Neural Networks: A Case Study	p. 301
Introduction	p. 302
Quantum neural networks	p. 303
Wind profilers	p. 306
Formulation of the bird removal problem	p. 309
Experimental results	p. 313
Conclusions	p. 325
Networks of Spiking Neurons in Data Mining	p. 329
Introduction	p. 330
Graph algorithms	p. 332
Clustering	p. 336
Critical path method	p. 340
The longest common subsequence	p. 342
Conclusions	p. 344
Genetic Algorithms, Pattern Classification and Neural Networks Design	p. 347
Introduction	p. 348
Overview of genetic algorithms	p. 351
Description of the genetic classifiers	p. 353
Determination of MLP architecture	p. 366
Discussion and conclusions	p. 375
Rough Sets in Pattern Recognition	p. 385
Basic rough set approach	p. 385
Searching for knowledge	p. 390
Hybrid methods	p. 416
Conclusions	p. 416
Combining Classifiers: Soft Computing Solutions	p. 427
Introduction	p. 427
Classifier combination	p. 428
Soft computing in classifier combination	p. 434
Conclusions	p. 445
Automated Generation of Qualitative Representations of Complex Objects by Hybrid Soft-Computing Methods	p. 453
Introduction	p. 454
Problem	p. 456
Approach	p. 460
Conclusions	p. 472
Neuro-Fuzzy Models for Feature Selection and Classification	p. 475
Introduction	p. 475
A brief review	p. 477
Neuro-fuzzy methods for feature selection	p. 480
Neuro-fuzzy knowledge-based classification	p. 490
Results	p. 494
Conclusions and Discussion	p. 497
Adaptive Segmentation Techniques for Hyperspectral Imagery	p. 507
Introduction	p. 508
Hyperspectral imaging system	p. 510
Segmentation of hyperspectral imagery	p. 510
Adaptive segmentation based on iterative local feature extraction	p. 512
Adaptive unsupervised segmentation	p. 522
Conclusions	p. 528
Pattern Recognition Issues in Speech Processing	p. 531
Introduction	p. 531
Nature of speech signal	p. 535
Feature extraction in speech	p. 539
Pattern recognition models for speech recognition	p. 541
Challenges in pattern recognition tasks in speech	p. 549
Writing Speed and Writing Sequence Invariant On-Line Handwriting Recognition	p. 559
Introduction	p. 559
Writing speed invariance	p. 562
Writing sequence invariance	p. 568
Recognizer	p. 572
Conclusions	p. 572
Tongue Diagnosis Based on Biometric Pattern Recognition Technology	p. 575
Introduction	p. 576
Tongue image capturing	p. 582
Segmentation of tongue images	p. 583
Tongue feature extraction	p. 590
Tongue classification	p. 594
Conclusions	p. 595
Index	p. 599
About the editors	p. 611
Table of Contents provided by Syndetics. All Rights Reserved.

Pattern Recognition

From Classical To Modern Approaches

At a Glance

Hardcover

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How to return your order

Defective items

You Can Find This Book In

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