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984 Seiten
Englisch
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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:...mehr
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KlappentextThis 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.
ZusammenfassungConsiders classical and theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. This book provides an self-contained volume encapsulating this spectrum of information.
Details
ISBN978-1-59749-272-0
ProduktartBuch
Einbandart / SetinhaltGebunden
Verlag/Label
Erscheinungsjahr2008
Erscheinungsdatum01.07.2008
Auflage4. Aufl.
Seiten984 Seiten
SpracheEnglisch
Artikel-Nr.5625135

Inhalt/Kritik

Inhaltsverzeichnis1. 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.
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Kritik"This book is an excellent reference for pattern recognition, machine learning, and data mining. It focuses on the problems of classification and clustering, the two most important general problems in these areas. This book has tremendous breadth and depth in its coverage of these topics; it is clearly the best book available on the topic today. The new edition is an excellent up-to-date revision of the book....mehr

Autor

Sergios Theodoridis acquired a Physics degree with honors from the University of Athens, Greece in 1973 and a MSc and a Ph.D. degree in Signal Processing and Communications from the University of Birmingham, UK in 1975 and 1978 respectively. Since 1995 he has been a Professor with the Department of Informatics and Communications at the University of Athens. Konstantinos Koutroumbas acquired a degree from the University of Patras, Greece in Computer Engineering and Informatics in 1989, a MSc in Computer Science from the University of London, UK in 1990, and a Ph.D. degree from the University of Athens in 1995. Since 2001 he has been with the Institute for Space Applications and Remote Sensing of the National Observatory of Athens.

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