Pattern Recognition / Edition 4

Pattern Recognition / Edition 4

by Sergios Theodoridis, Konstantinos Koutroumbas
     
 

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

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Overview

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.

· More Matlab code is available, together with an accompanying manual, via this site

· 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.

· An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869).

  • 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
  • An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913)
  • 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
  • Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at www.textbooks.elsevier.com and search on "Theodoridis" to access resources for instructor.
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    Product Details

    ISBN-13:
    9781597492720
    Publisher:
    Elsevier Science
    Publication date:
    11/03/2008
    Edition description:
    New Edition
    Pages:
    984
    Product dimensions:
    7.80(w) x 9.30(h) x 2.20(d)

    Table of Contents

    1. Introduction
    2. Classifiers based on Bayes Decision
    3. Linear Classifiers
    4. Nonlinear Classifiers
    5. Feature Selection
    6. Feature Generation I: Data Transformation and Dimensionality Reduction
    7. Feature Generation II
    8. Template Matching
    9. Context Depedant Clarification
    10. System Evaultion
    11. Clustering: Basic Concepts
    12. Clustering Algorithms: Algorithms L Sequential
    13. Clustering Algorithms II: Hierarchical
    14. Clustering Algorithms III: Based on Function Optimization
    15. Clustering Algorithms IV: Clustering
    16. Cluster Validity

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