Multi-objective machine learning / Yaochu Jin (ed.).
"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 particula...
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin :
Springer,
[2006]
|
| Series: | Studies in computational intelligence ;
v. 16. |
| Subjects: |
| Summary: | "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."--Publisher description. |
|---|---|
| Physical Description: | xiii, 660 pages : illustrations ; 24 cm. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 3540306765 9783540306764 |