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.
Produkteigenschaften
- Artikelnummer: 9783642067969
- Medium: Buch
- ISBN: 978-3-642-06796-9
- Verlag: Springer
- Erscheinungstermin: 22.11.2010
- Sprache(n): Englisch
- Auflage: 1. Auflage. Softcover version of original hardcover Auflage 2006
- Serie: Studies in Computational Intelligence
- Produktform: Kartoniert, Paperback
- Gewicht: 1007 g
- Seiten: 660
- Format (B x H x T): 155 x 235 x 37 mm
- Ausgabetyp: Kein, Unbekannt