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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning

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Multi-Objective Machine Learning (Hardcover, 2006 ed.) Loot Price: R5,972
Discovery Miles 59 720
Multi-Objective Machine Learning (Hardcover, 2006 ed.): Yaochu Jin

Multi-Objective Machine Learning (Hardcover, 2006 ed.)

Yaochu Jin

Series: Studies in Computational Intelligence, 16

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Loot Price R5,972 Discovery Miles 59 720 | Repayment Terms: R560 pm x 12*

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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.

General

Imprint: Springer-Verlag
Country of origin: Germany
Series: Studies in Computational Intelligence, 16
Release date: February 2006
First published: 2006
Editors: Yaochu Jin
Dimensions: 235 x 155 x 36mm (L x W x T)
Format: Hardcover
Pages: 660
Edition: 2006 ed.
ISBN-13: 978-3-540-30676-4
Categories: Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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LSN: 3-540-30676-5
Barcode: 9783540306764

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