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| PATTERN RECOGNITION
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 To order this title, and for more information, click here
Third Edition
By
Sergios Theodoridis, Department of Informatics and Telecommunications, University of Athens, Greece
Konstantinos Koutroumbas, Institute for Space Applications & Remote Sensing, National Observatory of Athens, Greece
Description
A classic -- offering comprehensive and unified coverage with a balance between theory and practice!
Pattern recognition is integral
to a wide spectrum of scientific disciplines and technologies including image analysis, speech recognition, audio classification, communications,
computer-aided diagnosis, and data mining. The authors, leading experts in the field of pattern recognition, have once again provided
an up-to-date, self-contained volume encapsulating this wide spectrum of information.
Each chapter is designed to begin with basics
of theory progressing to advanced topics and then discusses cutting-edge techniques. Problems and exercises are present at the end of
each chapter with a solutions manual provided via a companion website where a number of demonstrations are also available to aid the
reader in gaining practical experience with the theories and associated algorithms.
This edition includes discussion of Bayesian classification,
Bayesian networks, linear and nonlinear classifier design (including neural networks and support vector machines), dynamic programming
and hidden Markov models for sequential data, feature generation (including wavelets, principal component analysis, independent component
analysis and fractals), feature selection techniques, basic concepts from learning theory, and clustering concepts and algorithms. 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.
FOR INSTRUCTORS: To obtain access to the solutions manual for this title
simply register on our textbook website (textbooks.elsevier.com)and request access to the Computer Science or Electronics and Electrical
Engineering subject area. Once approved (usually within one business day) you will be able to access all of the instructor-only materials
through the "Instructor Manual" link on this book's full web page.
Audience
Researchers, scientists, and engineering professionals working in communications and computer engineering; pattern recognition; informatics;
data mining; content-based data retrieval; automation; machine intelligence/machine vision; computer-aided medical diagnostics; speech
recognition; and image processing
Contents
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
Chapter 7: Feature Generation II
Chapter 8: Template Matching
Chapter 9:
Context-Dependant Classification
Chapter 10: System Evaluation
Chapter 11: Clustering: Basic Concepts
Chapter 12: Clustering Algorithms
I (Sequential)
Chapter 13: Clustering Algorithms II (Hierarchical)
Chapter 14: Clustering Algorithms III (Functional Optimization)
Chapter
15: Clustering Algorithms IV (Graph Theory)
Chapter 16: Cluster Validity
| Bibliographic details |
Hardbound, 856 pages, publication date: FEB-2006
ISBN-13: 978-0-12-369531-4
ISBN-10: 0-12-369531-7
Imprint: ACADEMIC PRESS
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| Price and Ordering |
Price:
USD 89.95
EUR 60.95
GBP 41.99
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Last update: 27 Sep 2008
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