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Ensemble Methods: Foundations and Algorithms (Chapman & Hall/CRC Data Mining and Knowledge Discovery Serie) 1st Edition

4.6 out of 5 stars 5 customer reviews
ISBN-13: 978-1439830031
ISBN-10: 1439830037
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Editorial Reviews

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 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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Product Details

  • Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Serie
  • Hardcover: 236 pages
  • Publisher: Chapman and Hall/CRC; 1 edition (June 6, 2012)
  • Language: English
  • ISBN-10: 1439830037
  • ISBN-13: 978-1439830031
  • Product Dimensions: 0.8 x 6.6 x 9.5 inches
  • Shipping Weight: 15.2 ounces (View shipping rates and policies)
  • Average Customer Review: 4.6 out of 5 stars  See all reviews (5 customer reviews)
  • Amazon Best Sellers Rank: #668,397 in Books (See Top 100 in Books)

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Top Customer Reviews

Format: Hardcover
Ensemble methods train multiple learners and then combine them for use. They have become a hot topic in academia since the 1990s, and are enjoying increased attention in industry. This is mainly based on their generalization ability, which is often much stronger than that of simple/base learners.

It is always exciting to receive a new book written by a prominent researcher such as Professor Zhi-Hua Zhou. Discussion in the book starts from a strong theoretical foundation, but the author also includes many references to successful applications, which makes it a good book both for the researcher and the practitioner. Moreover, this book is not written from a single point of view, but rather includes the view from pattern recognition, data mining as well as (to a lesser extent) statistics.

Important algorithms/approaches are discussed in pseudo-code, which facilitates the understanding. The author does not just provide the math, but also a clear explanation of the reasoning behind it. The discussion starts with the basic algorithm, and then lists a number of improvements that have been published in leading scientific journals.

What I missed in this book? Some of the statistical methods (logistic regression), references to software and hybrid ensembles. This should be seen as suggestions for a second edition of the book.
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Format: Hardcover Verified Purchase
This book provides a good survey on ensemble learning, and covers various interesting topics in ensemble learning. The references provided in this book are excellent. You can follow some related papers as suggested in the book to further investigate some topics. Since ensemble learning is very crucial to building practically useful model, I highly recommend this book to anyone who is interested in machine learning and data mining.
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Format: Hardcover Verified Purchase
This book is good for new users and also for the expert users it is good for extend the work from this book.
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Format: Hardcover
The idea of ensemble learning is to construct a pool of learners and combine them in smart way into an overall system, rather than to construct a monolithic system. Ensemble learning has become a popular machine learning approach during the last years. The present monograph authored by Professor Zhi-Hua Zhou is a valuable contribution to theoretical and practical ensemble learning. The material is very well presented, preliminaries and basic knowledge is discussed in detail, 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 the book!
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Format: Hardcover Verified Purchase
As a business/data analyst and a machine learning PhD student, I found this book is a great read for people interested in ensemble methods from different perspectives - industrial and research. Prof. Zhou's book provides an in-depth review of robust ensemble techniques with both theoretical and empirical analysis. The reference section is also a great supplementary material for students and practitioners. As a researcher, I really enjoyed reading the "Diversity" and the "Ensemble pruning" chapters. As a data analyst, I found Ensemble Methods is also a great reference book for programmers who need to implement ensemble algorithms.
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