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9781439830031

Ensemble Methods: Foundations and Algorithms (Chapman & Hall/Crc Machine Learnig & Pattern Recognition)

Zhou, Zhi-Hua

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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.

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About the Author:

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.

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Zhou, Zhi-Hua Author
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ISBN 10: 1439830037 ISBN 13: 9781439830031
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Book Description Taylor Francis Ltd, United States, 2012. Hardback. Book Condition: New. 236 x 152 mm. Language: English Brand New Book. 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. Bookseller Inventory # AAZ9781439830031

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Zhou, Zhi-Hua Author
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Book Description Taylor Francis Ltd, United States, 2012. Hardback. Book Condition: New. 236 x 152 mm. Language: English Brand New Book. 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. Bookseller Inventory # AAZ9781439830031

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Book Description Taylor & Francis Ltd. Hardback. Book Condition: new. BRAND NEW, Ensemble Methods: Foundations and Algorithms, Zhi-Hua Zhou, 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. Bookseller Inventory # B9781439830031

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Book Description Chapman and Hall/CRC, 2012. Book Condition: New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: "While the book is rather written for a machine learning and pattern recognition audience, the terminology is well explained and therefore also easily understandable for readers from other areas. In general the book is well structured and written and presents nicely the different ideas and approaches for combining single learners as well as their strengths and limitations." --Klaus Nordhausen, International Statistical Review (2013), 81 "Professor Zhou's book is a comprehensive introduction to ensemble methods in machine learning. It reviews the latest research in this exciting area. I learned a lot reading it!" --Thomas G. Dietterich, Professor and Director of Intelligent Systems Research, Oregon State University, Corvallis, USA; ACM Fellow; and Founding President of the International Machine Learning Society "This is a timely book. Right time and right book . with an authoritative but inclusive style that will allow many readers to gain knowledge on the topic." --Fabio Roli, University of Cagliari, Italy. Bookseller Inventory # ABE_book_new_1439830037

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