Combining Pattern Classifiers

Methods and Algorithms

Nonfiction, Science & Nature, Technology, Imaging Systems, Computers, Advanced Computing, Engineering, Computer Vision, Database Management
Cover of the book Combining Pattern Classifiers by Ludmila I. Kuncheva, Wiley
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Author: Ludmila I. Kuncheva ISBN: 9781118914540
Publisher: Wiley Publication: August 13, 2014
Imprint: Wiley Language: English
Author: Ludmila I. Kuncheva
ISBN: 9781118914540
Publisher: Wiley
Publication: August 13, 2014
Imprint: Wiley
Language: English

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition

The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of Combining Pattern Classifiers was published in 2004. Dr. Kuncheva has plucked from the rich landscape of recent classifier ensemble literature the topics, methods, and algorithms that will guide the reader toward a deeper understanding of the fundamentals, design, and applications of classifier ensemble methods.

Thoroughly updated, with MATLABĀ® code and practice data sets throughout, Combining Pattern Classifiers includes:

  • Coverage of Bayes decision theory and experimental comparison of classifiers
  • Essential ensemble methods such as Bagging, Random forest, AdaBoost, Random subspace, Rotation forest, Random oracle, and Error Correcting Output Code, among others
  • Chapters on classifier selection, diversity, and ensemble feature selection

With firm grounding in the fundamentals of pattern recognition, and featuring more than 140 illustrations, Combining Pattern Classifiers, Second Edition is a valuable reference for postgraduate students, researchers, and practitioners in computing and engineering.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition

The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of Combining Pattern Classifiers was published in 2004. Dr. Kuncheva has plucked from the rich landscape of recent classifier ensemble literature the topics, methods, and algorithms that will guide the reader toward a deeper understanding of the fundamentals, design, and applications of classifier ensemble methods.

Thoroughly updated, with MATLABĀ® code and practice data sets throughout, Combining Pattern Classifiers includes:

With firm grounding in the fundamentals of pattern recognition, and featuring more than 140 illustrations, Combining Pattern Classifiers, Second Edition is a valuable reference for postgraduate students, researchers, and practitioners in computing and engineering.

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