Articles liés à Multi-Objective Machine Learning
Synopsis
Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
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Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
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Multi-Objective Machine Learning
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Springer Berlin Heidelberg, 2010
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ISBN 13 : 9783642067969
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Multi-Objective Machine Learning
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Selected collection of recent research on multi-objective approach to machine learningRecent developments in evolutionary multi-objective optimizationApplies the concept of Pareto-optimality to machine learning Recently. N° de réf. du vendeur 5045885
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Multi-objective Machine Learning
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Multi-Objective Machine Learning (Studies in Computational Intelligence, 16)
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Multi-Objective Machine Learning
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Springer Berlin Heidelberg, 2010
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ISBN 13 : 9783642067969
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems. N° de réf. du vendeur 9783642067969
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Multi-Objective Machine Learning
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Springer Berlin Heidelberg Nov 2010, 2010
ISBN 10 : 3642067964
ISBN 13 : 9783642067969
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems. 676 pp. Englisch. N° de réf. du vendeur 9783642067969
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Multi-Objective Machine Learning
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Springer Berlin Heidelberg, Springer Berlin Heidelberg Nov 2010, 2010
ISBN 10 : 3642067964
ISBN 13 : 9783642067969
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 676 pp. Englisch. N° de réf. du vendeur 9783642067969
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Multi-Objective Machine Learning
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Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2010
ISBN 10 : 3642067964
ISBN 13 : 9783642067969
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Etat : New. Editor(s): Jin, Yaochu. Series: Studies in Computational Intelligence. Num Pages: 674 pages, 254 black & white illustrations, 104 black & white tables, biography. BIC Classification: UYQM. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 34. Weight in Grams: 1021. . 2010. 1st ed. Softcover of orig. ed. 2006. Paperback. . . . . N° de réf. du vendeur V9783642067969
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Multi-objective Machine Learning
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Springer, 2010
ISBN 10 : 3642067964
ISBN 13 : 9783642067969
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Multi-Objective Machine Learning (Studies in Computational Intelligence, 16)
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