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: |