Pattern Classification Using Ensemble Methods
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Ensemble Learning Pattern Classification Using Ensemble Methods Second Edition
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Author : Lior Rokach
language : en
Publisher: World Scientific
Release Date : 2019-02-27
Ensemble Learning Pattern Classification Using Ensemble Methods Second Edition written by Lior Rokach and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-27 with Computers categories.
This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced.Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized.The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.
Pattern Classification Using Ensemble Methods
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Author : Lior Rokach
language : en
Publisher: World Scientific
Release Date : 2009-11-30
Pattern Classification Using Ensemble Methods written by Lior Rokach and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-11-30 with Computers categories.
Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications.The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.
Ensemble Learning
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Author : Lior Rokach
language : en
Publisher: World Scientific Publishing Company
Release Date : 2019
Ensemble Learning written by Lior Rokach and has been published by World Scientific Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Algorithms categories.
This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced. Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized. The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.
Pattern Classification Using Ensemble Methods
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Author : Lior Rokach
language : en
Publisher: World Scientific
Release Date : 2010
Pattern Classification Using Ensemble Methods written by Lior Rokach and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Computers categories.
Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.
Multi Class Pattern Classification Using Neural Networks
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Author : Guobin Ou
language : en
Publisher:
Release Date : 2006
Multi Class Pattern Classification Using Neural Networks written by Guobin Ou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Computer network architectures categories.
Ensembles For Supervised Classification Learning
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Author : Ofer Matan
language : en
Publisher:
Release Date : 1997
Ensembles For Supervised Classification Learning written by Ofer Matan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Algorithms categories.
This dissertation studies the use of multiple classifiers (ensembles or committees) in learning tasks. Both theoretical and practical aspects of combining classifiers are studied.
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Author : 日本オペレーションズ・リサーチ学会
language : en
Publisher:
Release Date : 2008
written by 日本オペレーションズ・リサーチ学会 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Operations research categories.
Advanced Research On Mechanical Engineering Industry And Manufacturing Engineering
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Author : Helen Zhang
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2011-06-08
Advanced Research On Mechanical Engineering Industry And Manufacturing Engineering written by Helen Zhang and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-08 with Technology & Engineering categories.
Selected, peer reviewed papers from the 2011 International Conference on Mechanical Engineering, Industry and Manufacturing Engineering (MEIME 2011), July 23-24, 2011, Beijing, China
Neural Ensemble Learning With Application To Vehicle Fault Diagnostics
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Author : Ruoqian Liu
language : en
Publisher:
Release Date : 2009
Neural Ensemble Learning With Application To Vehicle Fault Diagnostics written by Ruoqian Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Algorithms categories.
Ensemble Methods
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Author : Zhi-Hua Zhou
language : en
Publisher: CRC Press
Release Date : 2025-02-15
Ensemble Methods written by Zhi-Hua Zhou and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-15 with Business & Economics categories.
Ensemble methods that train multiple learners and then combine them to use, with Boosting and Bagging as representatives, are well-known machine learning approaches. It has become common sense that an ensemble is usually significantly more accurate than a single learner, and ensemble methods have already achieved great success in various real-world tasks. Twelve years have passed since the publication of the first edition of the book in 2012 (Japanese and Chinese versions published in 2017 and 2020, respectively). Many significant advances in this field have been developed. First, many theoretical issues have been tackled, for example, the fundamental question of why AdaBoost seems resistant to overfitting gets addressed, so that now we understand much more about the essence of ensemble methods. Second, ensemble methods have been well developed in more machine learning fields, e.g., isolation forest in anomaly detection, so that now we have powerful ensemble methods for tasks beyond conventional supervised learning. Third, ensemble mechanisms have also been found helpful in emerging areas such as deep learning and online learning. This edition expands on the previous one with additional content to reflect the significant advances in the field, and is written in a concise but comprehensive style to be approachable to readers new to the subject.