Download PDF Ensemble Methods: Foundations and Algorithms (Chapman & Hall/Crc Machine Learnig & Pattern Recognition)
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Ensemble Methods: Foundations and Algorithms (Chapman & Hall/Crc Machine Learnig & Pattern Recognition)
Download PDF Ensemble Methods: Foundations and Algorithms (Chapman & Hall/Crc Machine Learnig & Pattern Recognition)
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Pressestimmen
"... 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
Über den Autor und weitere Mitwirkende
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.
Produktinformation
Gebundene Ausgabe: 236 Seiten
Verlag: Taylor & Francis Ltd (6. Juni 2012)
Sprache: Englisch
ISBN-10: 1439830037
ISBN-13: 978-1439830031
Größe und/oder Gewicht:
15,6 x 1,4 x 23,4 cm
Durchschnittliche Kundenbewertung:
5.0 von 5 Sternen
1 Kundenrezension
Amazon Bestseller-Rang:
Nr. 58.427 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
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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