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.
"synopsis" may belong to another edition of this title.
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.Review:
"... a valuable contribution to theoretical and practical ensemble learning. The material is very well presented, preliminaries and basic knowledge are discussed in detail, and many illustrations and pseudo-code tables help to understand the facts of this interesting field of research. Therefore, the book will become a helpful tool for practitioners working in the field of machine learning or pattern recognition as well as for students of engineering or computer sciences at the graduate and postgraduate level. I heartily recommend this book!"
?IEEE Computational Intelligence Magazine, February 2013
"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
"About this title" may belong to another edition of this title.
Book Description Book Condition: New. Depending on your location, this item may ship from the US or UK. Bookseller Inventory # 97814398300310000000
Book Description Book Condition: New. This item is Print on Demand - Depending on your location, this item may ship from the US or UK. Bookseller Inventory # POD_9781439830031
Book Description Taylor & Francis Group. Book Condition: Brand New. Dispatch Same Working Day, (Delivery 2-4 business days, Courier For Heavy/Expensive Items) Money Back Guarantee, 99.3% Customer Satisfaction, Prompt Customer Service. Bookseller Inventory # 41975538
Book Description Taylor and Francis, 2012. HRD. Book Condition: New. New Book. Shipped from US within 10 to 14 business days. Established seller since 2000. Bookseller Inventory # VT-9781439830031
Book Description Chapman and Hall/CRC, 2016. Paperback. Book Condition: New. PRINT ON DEMAND Book; New; Publication Year 2016; Not Signed; Fast Shipping from the UK. No. book. Bookseller Inventory # ria9781439830031_lsuk
Book Description Taylor & Francis Ltd, 2012. Book Condition: New. Num Pages: 236 pages, 73 black & white illustrations, 2 black & white tables. BIC Classification: UMB; UNF. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 156 x 236 x 18. Weight in Grams: 496. . 2012. 1st Edition. Hardcover. . . . . . Bookseller Inventory # V9781439830031
Book Description Taylor & Francis Ltd. Book Condition: New. Num Pages: 236 pages, 73 black & white illustrations, 2 black & white tables. BIC Classification: UMB; UNF. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 156 x 236 x 18. Weight in Grams: 496. . 2012. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland. Bookseller Inventory # V9781439830031
Book Description Chapman and Hall/CRC, 2012. HRD. Book Condition: New. New Book.Shipped from US within 10 to 14 business days.THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Bookseller Inventory # IP-9781439830031
Book Description Chapman and Hall/CRC, 2017. Hardback. Book Condition: NEW. 9781439830031 This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. Print on Demand title, produced to the highest standard, and there would be a delay in dispatch of around 10 working days. Bookseller Inventory # HTANDREE0250197
Book Description Chapman and Hall/CRC, 2012. HRD. Book Condition: New. New Book. Delivered from our US warehouse in 10 to 14 business days. THIS BOOK IS PRINTED ON DEMAND.Established seller since 2000. Bookseller Inventory # IP-9781439830031