Discriminant Analysis and Statistical Pattern Recognition

Discriminant Analysis and Statistical Pattern Recognition by Geoffrey J. McLachlan is published by Wiley-Interscience on August 4, 2004. This comprehensive volume spans 552 pages and is presented in English. The book offers a systematic account of discriminant analysis, emphasizing both theoretical and practical aspects, making it suitable for applied and theoretical statisticians as well as researchers in related fields.
Readers will find an in-depth exploration of modern advancements in discriminant techniques, including regularized discriminant analysis and bootstrap-based performance assessment. The text also addresses extensions of discriminant analysis relevant to statistical image analysis. With a bibliography containing over 1,200 references, this edition serves as a valuable resource for those engaged in pattern perception and statistical analysis.
Official synopsis Publisher
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.
“For both applied and theoretical statisticians as well as investigators working in the many areas in which relevant use can be made of discriminant techniques, this monograph provides a modern, comprehensive, and systematic account of discriminant analysis, with the focus on the more recent advances in the field.”
–SciTech Book News
“. . . a very useful source of information for any researcher working in discriminant analysis and pattern recognition.”
–Computational Statistics
Discriminant Analysis and Statistical Pattern Recognition provides a systematic account of the subject. 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. The accompanying bibliography contains over 1,200 references.
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