Pattern Recognition, Fourth Edition

  • 17h 30m
  • Konstantinos Koutroumbas, Sergios Theodoridis
  • Elsevier Science and Technology Books, Inc.
  • 2009

This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback.

  • Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques
  • Many more diagrams included--now in two color--to provide greater insight through visual presentation
  • Matlab code of the most common methods are given at the end of each chapter
  • Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms

In this Book

  • Introduction
  • Classifiers Based on Bayes Decision Theory
  • Linear Classifiers
  • Nonlinear Classifiers
  • Feature Selection
  • Feature Generation I—Data Transformation and Dimensionality Reduction
  • Feature Generation II
  • Template Matching
  • Context-Dependent Classification
  • Supervised Learning—The Epilogue
  • Clustering—Basic Concepts
  • Clustering Algorithms I—Sequential Algorithms
  • Clustering Algorithms II—Hierarchical Algorithms
  • Clustering Algorithms III—Schemes Based on Function Optimization
  • Clustering Algorithms IV
  • Cluster Validity