Item description for Multi-Objective Machine Learning (Studies in Computational Intelligence) (Studies in Computational Intelligence) 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.
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Est. Packaging Dimensions: Length: 9.45" Width: 6.14" Height: 1.18" Weight: 2.34 lbs.
Release Date Mar 1, 2006
ISBN 3540306765 ISBN13 9783540306764
Availability 103 units. Availability accurate as of May 27, 2017 06:01.
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