NDLI: A quotient space reduction for large-scale support vector machine datasets

Content Provider	IEEE Xplore Digital Library
Author	Xi, Qin Yi-dan, Su
Copyright Year	2011
Description	Author affiliation: Computer Science Department School of Computer and Information, Guangxi University Nanning, China (Yi-dan, Su) \|\| Department of Computer Engineering Guangxi University of Technology Liuzhou Guangxi, China (Xi, Qin)
Abstract	Againsting the low efficiency of training on large-scale support vector machine, use quotient space method to deduce a new sample reduction method, quotient space reduction(QSR). Unlike the traditional reduction methods, QSR brings the concept of “Granularity” into reduction method. It has two stages: CGR and FGR. In CGR, use KDC reduction to build the quotient space of original problem set with coarse grain-size. In FGR, build another quotient space with more fine grain-size. By changing the reduced intensity according to the number of redundant points remained, QSR gives a higher compression ratio and accuracy. Apply the new method to the large scale SVM social spam detection model. The detection model speed up obviously.
Starting Page	1
Ending Page	4
File Size	218834
Page Count	4
File Format	PDF
ISBN	9781424486915
e-ISBN	9781424486946
DOI	10.1109/ICEBEG.2011.5881675
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Accuracy Reduction Computational modeling Clustering algorithms Data models SVM Quotient space Granularity Kernel
Content Type	Text
Resource Type	Article

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