Pattern Recognition

ISBN-10: 1597492728

ISBN-13: 9781597492720

Edition: 4th 2008

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Description:

This book considers classical and current theory and practice, of both supervised and unsupervised 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. This edition includes many more worked examples and diagrams (in two colour) to help give greater understanding of the methods and their application. Computer-based problems will be included with MATLAB code. An accompanying book contains extra worked examples and MATLAB code of all the examples used in this book. Thoroughly developed to include many more worked examples to give greater understanding of this mathematically oriented subject Many more diagrams included now in two colour - to provide greater insight through visual presentation An accompanying manual includes MATLAB code of the methods and algorithms in the book, together with solved problems and real-life data sets in medial imaging, remote sensing and audio recognition. Latest hot topics included to further the reference value of the text
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Book details

List price: $109.00
Edition: 4th
Copyright year: 2008
Publisher: Elsevier Science & Technology Books
Publication date: 10/20/2008
Binding: Hardcover
Pages: 984
Size: 7.50" wide x 9.25" long x 2.00" tall
Weight: 4.048
Language: English

Introduction
Classifiers based on Bayes Decision
Linear Classifiers
Nonlinear Classifiers
Feature Selection
Feature Generation I: Data Transformation and Dimensionality Reduction
Feature Generation II
Template Matching
Context Depedant Clarification
System Evaultion
Clustering: Basic Concepts
Clustering Algorithms: Algorithms L Sequential
Clustering Algorithms II: Hierarchical
Clustering Algorithms III: Based on Function Optimization
Clustering Algorithms IV: Clustering
Cluster Validity
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