| 書目名稱 | Multi-Objective Machine Learning |
| 編輯 | Yaochu Jin |
| 視頻video | http://file.papertrans.cn/640/639986/639986.mp4 |
| 概述 | Selected collection of recent research on multi-objective approach to machine learning.Recent developments in evolutionary multi-objective optimization.Applies the concept of Pareto-optimality to mach |
| 叢書名稱 | Studies in Computational Intelligence |
| 圖書封面 |  |
| 描述 | .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. . |
| 出版日期 | Book 2006 |
| 關(guān)鍵詞 | Support Vector Machine; decision tree; evolution; fuzzy; fuzzy system; fuzzy systems; genetic algorithms; i |
| 版次 | 1 |
| doi | https://doi.org/10.1007/3-540-33019-4 |
| isbn_softcover | 978-3-642-06796-9 |
| isbn_ebook | 978-3-540-33019-6Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer-Verlag Berlin Heidelberg 2006 |
|Archiver|手機版|小黑屋|
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