Discriminant Analysis and Statistical Pattern Recognition
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From the Inside Flap
"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."
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.
- Publisher : Wiley-Interscience (August 4, 2004)
- Language : English
- Paperback : 526 pages
- ISBN-10 : 0471691151
- ISBN-13 : 978-0471691150
- Item Weight : 1.83 pounds
- Dimensions : 5.9 x 1.5 x 8.8 inches
- Best Sellers Rank: #6,209,521 in Books (See Top 100 in Books)
- Customer Reviews:
About the author
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Discriminant analysis and pattern recognition are very similar topics. The term discriminant analysis is common in the statistical literature while pattern recognition is more common in the electrical engineering literature. McLachlan is scholarly and familiar with the literature in both disciplines (not common). He includes over 1200 references with many references from the late 1980s.
Professor McLachlan has been a key contributor to the literature on error rate estimation in discriminant analysis and devotes a great deal of coverage to this important topic. He also includes recent developments on bootstrap methods and summarizes the literature on bootstrap methods for adjusting bias in error rate estimation.
Much of the bootstrap work on error rate estimation involves comparative simulation studies, particular when training sample sizes are small. McLachlan provides a nice summary of the his work, the work of Efron and he also includes discussion of a couple of my simulation studies co-authored with Murthy and Nealy.