Multi-objective machine learning / Yaochu Jin (ed.).
"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 particula...
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| Other Authors: | |
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| Format: | Ebook |
| Language: | English |
| Published: |
Berlin :
Springer,
©2006.
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| Series: | Studies in computational intelligence ;
v. 16. |
| Subjects: | |
| Online Access: | Springer eBooks |
| Summary: | "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."--Publisher's website. |
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| Physical Description: | 1 online resource (xiii, 660 pages) : illustrations |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9783540330196 3540330194 3540306765 9783540306764 1280610611 9781280610615 6610610614 9786610610617 |