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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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Multi-Objective Machine Learning (Studies in Computational Intelligence, 16)
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Multi-Objective Machine Learning (Paperback)
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Paperback. Condition: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783642067969
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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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Condition: 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. Seller Inventory # 5045885
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Multi-Objective Machine Learning
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Taschenbuch. Condition: 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. Seller Inventory # 9783642067969
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Multi-Objective Machine Learning
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ISBN 10: 3642067964
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Condition: 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. . . . . Seller Inventory # V9783642067969
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Multi-Objective Machine Learning
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Multi-Objective Machine Learning
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