Multi-Objective Machine Learning

Yaochu Jin

ISBN 10: 3642067964 / ISBN 13: 9783642067969

Published by Springer, 2010

About this Item

Multi-Objective Machine Learning | Yaochu Jin | Taschenbuch | xiv | Englisch | 2010 | Springer | EAN 9783642067969 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 107211502

Bibliographic Details

• Title: Multi-Objective Machine Learning
• Publisher: Springer
• Publication Date: 2010
• Language: English
• ISBN 10: 3642067964
• ISBN 13: 9783642067969
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 37D millimeters 235 millimeters width by 155 millimeters height by 37 millimeters depth
• Weight: 1007 grams
• Seller catalogs:
  • Bücher

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.