Multi-Objective Machine Learning (Studies in Computational Intelligence, 16)

Jin, Yaochu [Editor]

ISBN 10: 3540306765 / ISBN 13: 9783540306764

Published by Springer, 2006

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Bibliographic Details

• Title: Multi-Objective Machine Learning (Studies in Computational Intelligence, 16)
• Publisher: Springer
• Publication Date: 2006
• Language: English
• ISBN 10: 3540306765
• ISBN 13: 9783540306764
• Binding: hardcover
• Condition: New
• Weight: 87 ounces

About this title

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.