| 书目名称 | Multi-Objective Machine Learning | | 编辑 | Yaochu Jin | | 视频video | | | 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 | | 丛书名称 | Studies in Computational Intelligence | | 图书封面 |  | | 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 | | 关键词 | 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 |
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发表于 2025-3-21 17:03:30
