Combining Pattern Classifiers: Methods and Algorithms

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Combining Pattern Classifiers: Methods and Algorithms by Ludmila I. Kuncheva.pdf

Uploaded : 2018/05/25 

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Descriptions : 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 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 R 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 illustrations Combining Pattern Classifiers Second Edition is a valuable reference for postgraduate students researchers and practitioners in computing and engineering










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