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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
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Multi-Objective Machine Learning
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Condition: New. The multi-objective approach to machine learning is particularly successful to improve the performance of traditional single objective machine learning methods, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This book presents a selected collection of research work on this approach. 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 36. Weight in Grams: 1116. . 2006. Hardback. . . . . Seller Inventory # V9783540306764
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