Multi-Objective Machine Learning
(Sprache: Englisch)
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...
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Klappentext zu „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.
Inhaltsverzeichnis zu „Multi-Objective Machine Learning “
- Multi-objective Feature Selection- Multi-objective Model Selection for Better Accuracy
- Multi-objective Learning for Better Interpretability
- Generation of Ensembles Using Multi-objective Optimization
- Applications of Multi-objective Machine Learning.
Bibliographische Angaben
- 2006, 2006, 660 Seiten, 254 Schwarz-Weiß-Abbildungen, Maße: 16,3 x 24,1 cm, Gebunden, Englisch
- Herausgegeben: Yaochu Jin
- Verlag: Springer
- ISBN-10: 3540306765
- ISBN-13: 9783540306764
- Erscheinungsdatum: 10.02.2006
Sprache:
Englisch
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