Ludmila I.Kuncheva – Combining Pattern Classifiers

Ludmila I.Kuncheva – Combining Pattern Classifiers

Description

A unified, coherent therapy of present classifier ensemble strategies, from fundamentals of sample recognition to ensemble characteristic choice, now in its second version

The artwork and science of mixing sample classifiers has flourished right into a prolific self-discipline because the first version ofCombining Pattern Classifiers was printed in 2004. Dr. Kuncheva has plucked from the wealthy panorama of latest classifier ensemble literature the subjects, strategies, and algorithms that can information the reader towards a deeper understanding of the basics, design, and purposes of classifier ensemble strategies.

Thoroughly up to date, with MATLAB code and follow knowledge units all through, Combining Pattern Classifiers consists of:

• Coverage of Bayes determination idea and experimental comparability of classifiers
• Essential ensemble strategies akin to Bagging, Random forest, AdaBoost, Random subspace, Rotation forest, Random oracle, and Error Correcting Output Code, amongst others
• Chapters on classifier choice, range, and ensemble characteristic choice

With agency grounding within the fundamentals of sample recognition, and that includes greater than 140 illustrations, Combining Pattern Classifiers, Second Edition is a helpful reference for postgraduate college students, researchers, and practitioners in computing and engineering.

Author Information

Ludmila Kuncheva is a Professor of Computer Science at Bangor University, United Kingdom. She has acquired 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 a number of classifier techniques.

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