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Multi-Objective Machine Learning (Studies in Computational Intelligence)
Multi-Objective Machine Learning (Studies in Computational Intelligence) Summary:By Yaochu Jin
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. password: gigle.ws password: gigle.ws NEWER EBOOKS
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Sponsored LinksMulti-Objective Machine Learning (Studies in Computational Intelligence) Keywordsmulti objective learning approach successful systems selection shown networks models presents monograph selected collection research computational fuzzy accuracy interpretability neural intelligence including feature machines multi objective approach learning methods multi objective optimization evolutionary multi objective computational intelligence successful developments learning studies |
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