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zhou zhi-hua - ensemble methods

Ensemble Methods Foundations and Algorithms




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Dettagli

Genere:Libro
Lingua: Inglese
Pubblicazione: 07/2012
Edizione: 1° edizione





Note Editore

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.




Sommario

IntroductionBasic Concepts Popular Learning AlgorithmsEvaluation and Comparison Ensemble Methods Applications of Ensemble Methods BoostingA General Boosting Procedure The AdaBoost Algorithm Illustrative Examples Theoretical IssuesMulticlass Extension Noise Tolerance BaggingTwo Ensemble Paradigms The Bagging Algorithm Illustrative Examples Theoretical Issues Random Tree Ensembles Combination MethodsBenefits of Combination AveragingVotingCombining by Learning Other Combination Methods Relevant Methods DiversityEnsemble Diversity Error DecompositionDiversity Measures Information Theoretic DiversityDiversity Generation Ensemble PruningWhat Is Ensemble Pruning Many Could Be Better Than All Categorization of Pruning Methods Ordering-Based Pruning Clustering-Based Pruning Optimization-Based Pruning Clustering EnsemblesClusteringCategorization of Clustering Ensemble Methods Similarity-Based Methods Graph-Based Methods Relabeling-Based Methods Transformation-Based Methods Advanced TopicsSemi-Supervised Learning Active Learning Cost-Sensitive Learning Class-Imbalance Learning Improving Comprehensibility Future Directions of Ensembles References Index Further Readings appear at the end of each chapter.




Autore

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.










Altre Informazioni

ISBN:

9781439830031

Condizione: Nuovo
Collana: Chapman & Hall/Crc Machine Learnig & Pattern Recognition
Dimensioni: 9.25 x 6.25 in Ø 1.15 lb
Formato: Copertina rigida
Illustration Notes:73 b/w images and 2 tables
Pagine Arabe: 236


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