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Item Details
| Title:
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MULTI-OBJECTIVE MACHINE LEARNING
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| By: |
Yaochu Jin (Editor) |
| Format: |
Hardback |
| List price:
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£249.99 |
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We currently do not stock this item, please contact the publisher directly for
further information.
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| ISBN 10: |
3540306765 |
| ISBN 13: |
9783540306764 |
| Publisher: |
SPRINGER-VERLAG BERLIN AND HEIDELBERG GMBH & CO. KG |
| Pub. date: |
10 February, 2006 |
| Edition: |
2006 ed. |
| Series: |
Studies in Computational Intelligence 16 |
| Pages: |
660 |
| Description: |
The multi-objective approach to machine learning is particularly successful to improve the performance of traditional single objective machine learning methods, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This book presents a selected collection of research work on this approach. |
| Synopsis: |
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. |
| Illustrations: |
104 Tables, black and white; 254 Illustrations, black and white; XIV, 660 |
| Publication: |
Germany |
| Imprint: |
Springer-Verlag Berlin and Heidelberg GmbH & Co. K |
| Returns: |
Returnable |
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