Pattern Recognition

Voorkant
Academic Press, 7 apr. 2006 - 856 pagina's
3 Recensies
Pattern recognition is a fast growing area with applications in a widely diverse number of fields such as communications engineering, bioinformatics, data mining, content-based database retrieval, to name but a few. This new edition addresses and keeps pace with the most recent advancements in these and related areas. This new edition: a) covers Data Mining, which was not treated in the previous edition, and is integrated with existing material in the book, b) includes new results on Learning Theory and Support Vector Machines, that are at the forefront of today's research, with a lot of interest both in academia and in applications-oriented communities, c) for the first time treats audio along with image applications since in today's world the most advanced applications are treated in a unified way and d) the subject of classifier combinations is treated, since this is a hot topic currently of interest in the pattern recognition community.

* The latest results on support vector machines including v-SVM's and their geometric interpretation
* Classifier combinations including the Boosting approach
* State-of-the-art material for clustering algorithms tailored for large data sets and/or high dimensional data, as required by applications such as web-mining and bioinformatics
* Coverage of diverse applications such as image analysis, optical character recognition, channel equalization, speech recognition and audio classification
 

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LibraryThing Review

Gebruikersrecensie  - juha - LibraryThing

This is a book I keep coming back to as a reference. Some areas are discussed fairly briefly, but clustering, for instance, is labored in four chapters. Volledige recensie lezen

Inhoudsopgave

1 INTRODUCTION
1
2 CLASSIFIERS BASED ON BAYES DECISION THEORY
13
3 LINEAR CLASSIFIERS
69
4 NONLINEAR CLASSIFIERS
121
5 FEATURE SELECTION
213
LINEAR TRANSFORMS
263
7 FEATURE GENERATION II
327
8 TEMPLATE MATCHING
397
SEQUENTIAL ALGORITHMS
517
HIERARCHICAL ALGORITHMS
541
SCHEMES BASED ON FUNCTION OPTIMIZATION
589
15 CLUSTERING ALGORITHMS IV
653
16 CLUSTER VALIDITY
733
Appendix A HINTS FROM PROBABILITY AND STATISTICS
785
Appendix B LINEAR ALGEBRA BASICS
797
Appendix C COST FUNCTION OPTIMIZATION
801

9 CONTEXTDEPENDENT CLASSIFICATION
427
10 SYSTEM EVALUATION
471
BASIC CONCEPTS
483
Appendix D BASIC DEFINITIONS FROM LINEAR SYSTEMS THEORY
819
INDEX
823
Copyright

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Pagina 65 - A simple algorithm for nearest neighbor search in high dimensions", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.

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Over de auteur (2006)

Sergios Theodoridis is Professor of Signal Processing and Machine Learning in the Department of Informatics and Telecommunications of the University of Athens.
He is the co-author of the bestselling book, Pattern Recognition, and the co-author of Introduction to Pattern Recognition: A MATLAB Approach.
He serves as Editor-in-Chief for the IEEE Transactions on Signal Processing, and he is the co-Editor in Chief with Rama Chellapa for the Academic
Press Library in Signal Processing.

He has received a number of awards including the 2014 IEEE Signal Processing Magazine Best Paper Award, the 2009 IEEE Computational Intelligence Society Transactions on Neural Networks Outstanding Paper Award, the 2014 IEEE Signal Processing Society Education Award, the EURASIP 2014 Meritorious Service Award, and he has served as a Distinguished Lecturer for the IEEE Signal Processing Society and the IEEE Circuits and Systems Society. He is a Fellow of EURASIP and a Fellow of IEEE.

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.

Bibliografische gegevens