Combining Pattern Classifiers

<b>A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition</b> <p>The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of <i>Combining Pattern Classifiers</i> 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.</p> <p>Thoroughly updated, with MATLAB® code and practice data sets throughout, <i>Combining Pattern Classifiers</i> includes:</p> <ul> <li>Coverage of Bayes decision theory and experimental comparison of classifiers</li> <li>Essential ensemble methods such as Bagging, Random forest, AdaBoost, Random subspace, Rotation forest, Random oracle, and Error Correcting Output Code, among others</li> <li>Chapters on classifier selection, diversity, and ensemble feature selection</li> </ul> <p>With firm grounding in the fundamentals of pattern recognition, and featuring more than 140 illustrations, <i>Combining Pattern Classifiers, Second Edition</i> is a valuable reference for postgraduate students, researchers, and practitioners in computing and engineering.</p>