Combining Pattern Classifiers : Methods and Algorithms

Ludmila I Kuncheva

ISBN 10: 1118315235 / ISBN 13: 9781118315231

Published by Wiley Sep 2014, 2014

About this Item

Neuware - A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second editionThe 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(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 selectionWith 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.

Seller Inventory # 9781118315231

Bibliographic Details

• Title: Combining Pattern Classifiers : Methods and Algorithms
• Publisher: Wiley Sep 2014
• Publication Date: 2014
• Language: English
• ISBN 10: 1118315235
• ISBN 13: 9781118315231
• Binding: Buch
• Edition: 2nd Edition
• Condition: Neu
• Dimensions: 240W x 161H x 25D millimeters 240 millimeters width by 161 millimeters height by 25 millimeters depth
• Weight: 736 grams

About this title

Synopsis

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.

About the Author

Ludmila Kuncheva is a Professor of Computer Science at Bangor University, United Kingdom. She has received two IEEE Best Paper awards. In 2012, Dr. Kuncheva was awarded a Fellowship to the International Association for Pattern Recognition (IAPR) for her contributions to multiple classifier systems.