Ensemble Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Zhou, Zhi-Hua

ISBN 10: 1439830037 / ISBN 13: 9781439830031

Verlag: Chapman and Hall/CRC, 2012

Beschreibung

236 pp., hardcover, previous owner's name neatly inked to the title page, else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

Bestandsnummer des Verkäufers ZB1317015

Bibliografische Details

• Titel: Ensemble Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
• Verlag: Chapman and Hall/CRC
• Veröffentlichungsdatum: 2012
• Sprache: Englisch
• ISBN-10: 1439830037
• ISBN-13: 9781439830031
• Bindung: Hardcover
• Zustand: Fine

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

Zhi-Hua Zhou is a professor in the Department of Computer Science and Technology and the National Key Laboratory for Novel Software Technology at Nanjing University. Dr. Zhou is the founding steering committee co-chair of ACML and associate editor-in-chief, associate editor, and editorial board member of numerous journals. He has published extensively in top-tier journals, chaired many conferences, and won six international journal/conference/competition awards. His research interests encompass the areas of machine learning, data mining, pattern recognition, and multimedia information retrieval.