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.
| ISBN: | 9783642067969 |
| Publication date: | 22nd November 2010 |
| Author: | Yaochu Jin |
| Publisher: | Springer an imprint of Springer Berlin Heidelberg |
| Format: | Paperback |
| Pagination: | 660 pages |
| Series: | Studies in Computational Intelligence |
| Genres: |
Maths for engineers Cybernetics and systems theory Mathematical physics Artificial intelligence |
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.
Multi-Objective Machine Learning features in the following genres: Maths for engineers, Cybernetics and systems theory, Mathematical physics, Artificial intelligence
Multi-Objective Machine Learning is available in Paperback, Hardback
Multi-Objective Machine Learning was written by Yaochu Jin and published by Springer an imprint of Springer Berlin Heidelberg
Multi-Objective Machine Learning has 660 pages
Yes it is part of Studies in Computational Intelligence series