Discriminant Analysis and Statistical Pattern Recognition
Published on 24. August 2004
Book
Paperback/Softback
552 pages
978-0-471-69115-0 (ISBN)
Description
Provides a systematic account of the subject area, concentrating on the most recent advances in the field. While the focus is on practical considerations, both theoretical and practical issues are explored. Among the advances covered are: regularized discriminant analysis and bootstrap-based assessment of the performance of a sample-based discriminant rule and extensions of discriminant analysis motivated by problems in statistical image analysis. Includes over 1,200 references in the bibliography.
Reviews / Votes
" ... in my opinion (this book) has been proved .. to be a valuable resource (and) should not be overlooked by any scholarly library." (Journal of the Royal Statistical Society Series A, June 2005)More details
Product info
Paperback
Series
Edition
1. Auflage
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 32 mm
Weight
886 gr
ISBN-13
978-0-471-69115-0 (9780471691150)
Schweitzer Classification
Other editions
Additional editions
Geoffrey McLachlan
Discriminant Analysis and Statistical Pattern Recognition
E-Book
02/2005
Wiley
€121.99
Available for download
Geoffrey McLachan
Discriminant Analysis and Statistical Pattern Recognition
Book
04/1992
Wiley
€108.88
Article exhausted; check different version
Person
Geoffrey J. McLachlan, PhD, is Professor of Mathematics at the University of Queensland, Australia. He is the author, with David Peel, of Finite Mixture Models(Wiley) and, with Thriyambakam Krishnan, of The EM Algorithm and Extensions(Wiley), among others.
Content
Preface.
1. General Introduction.
2. Likelihood-Based Approaches to Discrimination.
3. Discrimination via Normal Models.
4. Distributional Results for Discrimination via Normal Models.
5. Some Practical Aspects and Variants of Normal Theory-Based Discriminant Rules.
6. Data Analytic Considerations with Normal Theory-Based Discriminant Analysis.
7. Parametric Discrimination via Nonnormal Models.
8. Logistic Discrimination.
9. Nonparametric Discrimination.
10. Estimation of Error Rates.
11. Assessing the Reliability of the Estimated Posterior Probabilities of Group Membership.
12. Selection of Feature Variables in Discriminan Analysis.
13. Statistical Image Analysis.
References.
Author Index.
Subject Index.