Bibliothèque de L'IMIST/CNRST

Front Cover; Pattern Recognition; Copyright Page; Contents; Preface; Chapter 1 Introduction; Chapter 2 Classifiers Based on Bayes Decision Theory; Chapter 3 Linear Classifiers; Chapter 4 Nonlinear Classifiers; Chapter 5 Feature Selection; Chapter 6 Feature Generation I: Data Transformation and Dimensionality Reduction; Chapter 7 Feature Generation II; Chapter 8 Template Matching; Chapter 9 Context-Dependent Classification; Chapter 10 Supervised Learning: The Epilogue; Chapter 11 Clustering: Basic Concepts; Chapter 12 Clustering Algorithms I: Sequential Algorithms.

Chapter 13 Clustering Algorithms II: Hierarchical AlgorithmsChapter 14 Clustering Algorithms III: Schemes Based on Function Optimization; Chapter 15 Clustering Algorithms IV; Chapter 16 Cluster Validity; Appendix A Hints from Probability and Statistics; Appendix B Linear Algebra Basics; Appendix C Cost Function Optimization; Appendix D Basic Definitions from Linear Systems Theory; Index.

The only book to combine coverage of classical topics with the most recent methods just developed, making it a complete resource on using all the techniques in pattern recognition today.