Combining Pattern Classifiers


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Combining Pattern Classifiers


Combining Pattern Classifiers

Author: Ludmila I. Kuncheva

language: en

Publisher: Wiley-Interscience

Release Date: 2004-07


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Publisher Description

Combining Pattern Classifiers


Combining Pattern Classifiers

Author: Ludmila I. Kuncheva

language: en

Publisher: John Wiley & Sons

Release Date: 2014-08-13


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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 MATLAB® 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.

Combining Pattern Classifiers


Combining Pattern Classifiers

Author: Ludmila I. Kuncheva

language: en

Publisher: John Wiley & Sons

Release Date: 2004-08-20


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Covering pattern classification methods, Combining Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that bring the text alive and demonstrate 'real-world' applications it is destined to become essential reading.