Buch, Englisch, 236 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 513 g
Reihe: Chapman & Hall/CRC Machine Learning & Pattern Recognition
Foundations and Algorithms
Buch, Englisch, 236 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 513 g
Reihe: Chapman & Hall/CRC Machine Learning & Pattern Recognition
ISBN: 978-1-4398-3003-1
Verlag: CRC Press
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.
Zielgruppe
Professional Practice & Development
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Introduction. Boosting. Bagging. Combination Methods. Diversity. Ensemble Pruning. Clustering Ensembles. Advanced Topics. References. Index.