.png){kind=icon}
返回
Large-scale data mining using genetics-based machine learning
.png){kind=icon}
PRE
AI
DOI：10.1002/widm.1078.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the last decade, genetics-based machine learning methods have shown their competence in large-scale data mining tasks because of the scalability capacity that these techniques have demonstrated. This capacity goes beyond the innate massive parallelism of evolutionary computation methods by the proposal of a variety of mechanisms specifically tailored for machine learning tasks, including knowledge representations that exploit regularities in the datasets, hardware accelerations or data-intensive computing methods, among others. This paper reviews different classes of methods that alone or (in many cases) combined accelerate genetics-based machine learning methods. (c) 2013 Wiley Periodicals, Inc.
Keyword:
CLASSIFIER-SYSTEMS
DISTRIBUTION ALGORITHMS
SUBSET-SELECTION
XCS
PREDICTION
ENSEMBLES
EVOLUTIONARY
PERFORMANCE
TAXONOMY
DOMAIN
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11.7
论文数:
555
被引数:
6.4K
机构
引用论文
.png){kind=icon}
分享收藏
Role of red mud as a cementing material in concrete: a comprehensive study on durability behavior
PREAI
分享收藏
查看更多内容