Combining Pattern Classifiers Methods and Algorithms
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Form:Einzelkauf Download
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Sprache:Englisch
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eBook Format:ePUB 3
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Produktdetails
Format
ePUB 3
Kopierschutz
Ja
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
13.08.2014
Verlag
John Wiley & Sons IncSeitenzahl
384 (Printausgabe)
Dateigröße
15271 KB
Auflage
2. Auflage
Sprache
Englisch
EAN
9781118914540
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.
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.
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