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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 (Studies in Computational Intelligence, 16)
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Multi-Objective Machine Learning (Studies in Computational Intelligence, 16)
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Multi-Objective Machine Learning (Studies in Computational Intelligence, 16)
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Multi-Objective Machine Learning (Hardcover)
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Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2006
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ISBN 13: 9783540306764
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Hardcover. Condition: new. Hardcover. 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 # 9783540306764
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Multi-Objective Machine Learning (Studies in Computational Intelligence, 16)
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