Multi-Objective Machine Learning (Studies in Computational Intelligence)

Jin, Yaochu (Editor)

ISBN 10: 3642067964 / ISBN 13: 9783642067969

Published by Springer Berlin Heidelberg, 2010

About this Item

660 pages. 9.25x6.10x1.53 inches. In Stock.

Seller Inventory # x-3642067964

Bibliographic Details

• Title: Multi-Objective Machine Learning (Studies in Computational Intelligence)
• Publisher: Springer Berlin Heidelberg
• Publication Date: 2010
• Language: English
• ISBN 10: 3642067964
• ISBN 13: 9783642067969
• Binding: Paperback
• Condition: Brand New
• Weight: 1.14 kilograms

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.