Ensemble Methods : Foundations and Algorithms (1439830037)

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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.

Product details

• Format Hardback | 236 pages
• Dimensions 156 x 234 x 14.22mm | 522g
• Publication date 13 Jul 2012
• Publisher Taylor & Francis Inc
• Publication City/Country Washington, United States
• Language English
• Illustrations note 2 Tables, black and white; 73 Illustrations, black and white
• ISBN10 1439830037
• ISBN13 9781439830031
• Bestsellers rank 1,683,361

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