Ensemble Methods - Foundations and Algorithms (Hardcover)

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.

General

Imprint:	Taylor & Francis
Country of origin:	United States
Series:	Chapman & Hall/CRC Machine Learning & Pattern Recognition
Release date:	June 2012
First published:	2012
Authors:	Zhi-Hua Zhou
Dimensions:	234 x 156 x 18mm (L x W x T)
Format:	Hardcover
Pages:	236
ISBN-13:	978-1-4398-3003-1
Categories:	Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Computing & IT > Applications of computing > Databases > Data mining Promotions
LSN:	1-4398-3003-7
Barcode:	9781439830031

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