Multi-Objective Machine Learning

Yaochu Jin

ISBN 10: 3642067964 / ISBN 13: 9783642067969

Verlag: Springer Berlin Heidelberg, 2010

Beschreibung

Druck auf Anfrage Neuware - Printed after ordering - 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.

Bestandsnummer des Verkäufers 9783642067969

Bibliografische Details

• Titel: Multi-Objective Machine Learning
• Verlag: Springer Berlin Heidelberg
• Erscheinungsjahr: 2010
• Sprache: Englisch
• ISBN-10: 3642067964
• ISBN-13: 9783642067969
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

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.