Pattern Classification Using Ensemble Methods
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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 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 : 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.
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.
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.
Ensemble Learning Algorithms With Python
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2021-04-26
Ensemble Learning Algorithms With Python written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-26 with Computers categories.
Predictive performance is the most important concern on many classification and regression problems. Ensemble learning algorithms combine the predictions from multiple models and are designed to perform better than any contributing ensemble member. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively improve predictive modeling performance using ensemble algorithms.
Research On E Learning And Ict In Education
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Author : Tassos Anastasios Mikropoulos
language : en
Publisher: Springer
Release Date : 2018-10-04
Research On E Learning And Ict In Education written by Tassos Anastasios Mikropoulos and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-04 with Education categories.
This book is an essential text for researchers and academics seeking the most comprehensive and up-to-date coverage of all aspects of e-learning and ICT in education, providing expanded peer-reviewed content from research presented at the 10th Panhellenic Conference on ICT in Education. The volume includes papers covering technical, pedagogical, organizational, instructional, as well as policy aspects of ICT in Education and e-Learning, and emphasizes applied research relevant to the educational realities in schools, colleges, universities and informal learning organizations. Research on e-Learning and ICT in Education is a valuable resource for education professionals interested in keeping up with current trends, perspectives, and approaches determining e-Learning and ICT integration in practice, including learning and teaching, curriculum and instructional design, learning media and environments, teacher education and professional development.
Ensemble Methods For Machine Learning
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Author : Gautam Kunapuli
language : en
Publisher: Simon and Schuster
Release Date : 2023-05-30
Ensemble Methods For Machine Learning written by Gautam Kunapuli and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-30 with Computers categories.
Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate. Inside Ensemble Methods for Machine Learning you will find: Methods for classification, regression, and recommendations Sophisticated off-the-shelf ensemble implementations Random forests, boosting, and gradient boosting Feature engineering and ensemble diversity Interpretability and explainability for ensemble methods Ensemble machine learning trains a diverse group of machine learning models to work together, aggregating their output to deliver richer results than a single model. Now in Ensemble Methods for Machine Learning you’ll discover core ensemble methods that have proven records in both data science competitions and real-world applications. Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems. About the Technology Automatically compare, contrast, and blend the output from multiple models to squeeze the best results from your data. Ensemble machine learning applies a “wisdom of crowds” method that dodges the inaccuracies and limitations of a single model. By basing responses on multiple perspectives, this innovative approach can deliver robust predictions even without massive datasets. About the Book Ensemble Methods for Machine Learning teaches you practical techniques for applying multiple ML approaches simultaneously. Each chapter contains a unique case study that demonstrates a fully functional ensemble method, with examples including medical diagnosis, sentiment analysis, handwriting classification, and more. There’s no complex math or theory—you’ll learn in a visuals-first manner, with ample code for easy experimentation! What’s Inside Bagging, boosting, and gradient boosting Methods for classification, regression, and retrieval Interpretability and explainability for ensemble methods Feature engineering and ensemble diversity About the Reader For Python programmers with machine learning experience. About the Author Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry. Table of Contents PART 1 - THE BASICS OF ENSEMBLES 1 Ensemble methods: Hype or hallelujah? PART 2 - ESSENTIAL ENSEMBLE METHODS 2 Homogeneous parallel ensembles: Bagging and random forests 3 Heterogeneous parallel ensembles: Combining strong learners 4 Sequential ensembles: Adaptive boosting 5 Sequential ensembles: Gradient boosting 6 Sequential ensembles: Newton boosting PART 3 - ENSEMBLES IN THE WILD: ADAPTING ENSEMBLE METHODS TO YOUR DATA 7 Learning with continuous and count labels 8 Learning with categorical features 9 Explaining your ensembles
Information Communication And Computing Technology
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Author : Saroj Kaushik
language : en
Publisher: Springer
Release Date : 2017-10-10
Information Communication And Computing Technology written by Saroj Kaushik and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-10 with Computers categories.
This book constitutes the refereed proceedings of the Second International Conference on Information, Communication and Computing Technology, ICICCT 2017, held in New Delhi, India, in May 2017. The 29 revised full papers and the 5 revised short papers presented in this volume were carefully reviewed and selected from 219 submissions. The papers are organized in topical sections on network systems and communication security; software engineering; algorithm and high performance computing.
Intelligent Information And Database Systems
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Author : Jeng-Shyang Pan
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-03-02
Intelligent Information And Database Systems written by Jeng-Shyang Pan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-02 with Computers categories.
The three-volume set LNAI 7196, LNAI 7197 and LNAI 7198 constitutes the refereed proceedings of the 4th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2012, held in Kaohsiung, Taiwan in March 2012. The 161 revised papers presented were carefully reviewed and selected from more than 472 submissions. The papers included cover the following topics: intelligent database systems, data warehouses and data mining, natural language processing and computational linguistics, semantic Web, social networks and recommendation systems, collaborative systems and applications, e-bussiness and e-commerce systems, e-learning systems, information modeling and requirements engineering, information retrieval systems, intelligent agents and multi-agent systems, intelligent information systems, intelligent internet systems, intelligent optimization techniques, object-relational DBMS, ontologies and knowledge sharing, semi-structured and XML database systems, unified modeling language and unified processes, Web services and semantic Web, computer networks and communication systems.