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

Hardcover

ISBN 10: 3540306765 ISBN 13: 9783540306764

Verlag: Springer, 2006

Inhaltsangabe

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.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2006
• Sprache: Englisch
• ISBN 10: 3540306765
• ISBN 13: 9783540306764
• Einband: Gebundene Ausgabe
• Anzahl der Seiten: 674
• Herausgeber: Jin Yaochu
• Kontakt zum Hersteller: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Tiergartenstr. 17; Heidelberg; 69121; Deutschland