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Multi-Objective Machine Learning

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Springer Berlin,
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Kurzbeschreibung

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
Schlagworte

Titel: Multi-Objective Machine Learning
Autoren/Herausgeber: Yaochu Jin (Hrsg.)
Aus der Reihe: Studies in Computational Intelligence
Ausgabe: Softcover reprint of hardcover 1st ed. 2006

ISBN/EAN: 9783642067969

Seitenzahl: 660
Format: 23,5 x 15,5 cm
Produktform: Taschenbuch/Softcover
Gewicht: 1,021 g
Sprache: Englisch

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