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Multi-Objective Machine Learning
210,95 €
+ 421 points
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.
Spécifications
Parties prenantes
- Editeur:
Contenu
- Nombre de pages :
- 660
- Langue:
- Anglais
- Collection :
- Tome:
- n° 16
Caractéristiques
- EAN:
- 9783642067969
- Date de parution :
- 22-11-10
- Format:
- Livre broché
- Format numérique:
- Trade paperback (VS)
- Dimensions :
- 156 mm x 234 mm
- Poids :
- 929 g
