Distributed Learning over Massive XML Documents in ELM Feature Space
Joint Authors
Wang, Guoren
Bi, Xin
Zhao, Xiangguo
Zhang, Zhen
Chen, Shuang
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-05-27
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
With the exponentially increasing volume of XML data, centralized learning solutions are unable to meet the requirements of mining applications with massive training samples.
In this paper, a solution to distributed learning over massive XML documents is proposed, 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 Extreme Learning Machine (ELM) feature space.
Extensive experiments are conducted on massive XML documents datasets to verify the effectiveness and efficiency for both classification and clustering applications.
American Psychological Association (APA)
Bi, Xin& Zhao, Xiangguo& Wang, Guoren& Zhang, Zhen& Chen, Shuang. 2015. Distributed Learning over Massive XML Documents in ELM Feature Space. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1075069
Modern Language Association (MLA)
Bi, Xin…[et al.]. Distributed Learning over Massive XML Documents in ELM Feature Space. Mathematical Problems in Engineering No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1075069
American Medical Association (AMA)
Bi, Xin& Zhao, Xiangguo& Wang, Guoren& Zhang, Zhen& Chen, Shuang. Distributed Learning over Massive XML Documents in ELM Feature Space. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1075069
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1075069