
Statistical Pattern Recognition
Andrew R. Webb(Author)
Wiley (Publisher)
2nd Edition
Published on 18. July 2002
Book
Paperback/Softback
514 pages
978-0-470-84514-1 (ISBN)
Article exhausted; check for reprint
Description
Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition.
Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems.
* Provides a self-contained introduction to statistical pattern recognition.
* Each technique described is illustrated by real examples.
* Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification.
* Each section concludes with a description of the applications that have been addressed and with further developments of the theory.
* Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability.
* Features a variety of exercises, from 'open-book' questions to more lengthy projects.
The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments.
Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems.
* Provides a self-contained introduction to statistical pattern recognition.
* Each technique described is illustrated by real examples.
* Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification.
* Each section concludes with a description of the applications that have been addressed and with further developments of the theory.
* Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability.
* Features a variety of exercises, from 'open-book' questions to more lengthy projects.
The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments.
More details
Edition
2., Auflage
Language
English
Place of publication
Chichester
United Kingdom
Publishing group
John Wiley and Sons Ltd
Target group
College/higher education
Professional and scholarly
Edition type
Revised edition
Illustrations
Ill.
Dimensions
Height: 24.3 cm
Width: 16.9 cm
Thickness: 30 mm
Weight
882 gr
ISBN-13
978-0-470-84514-1 (9780470845141)
Schweitzer Classification
Other editions
New editions

Andrew R. Webb | Keith Derek Copsey | Gavin Cawley
Statistical Pattern Recognition
Book
10/2011
3rd Edition
Wiley
€80.00
Shipment within 10-20 days

Andrew R. Webb
Statistical Pattern Recognition
Book
07/2002
2nd Edition
Wiley
€155.00
Article exhausted; check for reprint
Previous edition

Andrew Webb
Statistical Pattern Recognition
Book
08/1999
Butterworth-Heinemann
€58.37
No shipping information available
Content
Preface.
Notation.
Introduction to statistical pattern recognition.
Density estimation - parametric.
Density estimation - nonparametric.
Linear discriminant analysis
Nonlinear discriminant analysis - kernel methods.
Nonlinear discriminant analysis - projection methods.
Tree-based methods.
Performance.
Feature selection and extraction.
Clustering.
Additional topics.
Appendices.
References.
Index.
Notation.
Introduction to statistical pattern recognition.
Density estimation - parametric.
Density estimation - nonparametric.
Linear discriminant analysis
Nonlinear discriminant analysis - kernel methods.
Nonlinear discriminant analysis - projection methods.
Tree-based methods.
Performance.
Feature selection and extraction.
Clustering.
Additional topics.
Appendices.
References.
Index.