Produktnummer:
18565d56e2858a42b1abd337d063ffafb5
| Themengebiete: | Support Vector Machine decision tree evolution fuzzy fuzzy system fuzzy systems genetic algorithms intelligent systems learning machine learning |
|---|---|
| Veröffentlichungsdatum: | 10.02.2006 |
| EAN: | 9783540306764 |
| Sprache: | Englisch |
| Seitenzahl: | 660 |
| Produktart: | Gebunden |
| Herausgeber: | Jin, Yaochu |
| Verlag: | Springer Berlin |
Produktinformationen "Multi-Objective Machine Learning"
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.
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