| Multi-Objective Machine Learning 2006 Edition Contributor(s): Jin, Yaochu (Editor) |
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ISBN: 3540306765 ISBN-13: 9783540306764 Publisher: Springer OUR PRICE: $208.99 Product Type: Hardcover Published: February 2006 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Mathematics | Applied - Computers | Intelligence (ai) & Semantics - Science | Physics - Mathematical & Computational |
| Dewey: 006.31 |
| Series: Studies in Computational Intelligence |
| Physical Information: 1.44" H x 6.14" W x 9.21" (2.46 lbs) 660 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |