Distributed Learning over Massive XML Documents in ELM Feature Space

Distributed Learning over Massive XML Documents in ELM Feature Space
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ELM 特征空间中海量 XML 文档的分布式学习

DOI:
10.1155/2015/923097
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发表时间:
2015-05
影响因子:
--
通讯作者:
Chen, Shuang
Chen, Shuang
中科院分区:
工程技术4区
文献类型:
--
作者:
Zhao, Xiangguo;Wang, Guoren;Zhang, Zhen;Chen, Shuang

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由于集中学习解决方案无法满足大量培训样本的采矿应用要求,因此在本文中提出了对大量XML文档进行分配学习的解决方案,该文件提供了分布式转换O
Since centralized learning solutions are unable to meet the requirements of mining applications with massive training samples, a solution to distributed learning over massive XML documents is proposed in this paper, which provides distributed conversion of XML documents into representation model in parallel based on MapReduce, and a distributed learning component based on Extreme Learning Machine for mining tasks of classification or clustering. Within this framework, training samples are converted from raw XML datasets with better efficiency and information representation ability and taken to distributed learning algorithms in ELM feature space. Extensive experiments are conducted on massive XML documents datasets to verify the effectiveness and efficiency for both distributed classification and clustering applications.
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