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Support Vector Number Reduction: Survey and Experimental Evaluations

Title
Support Vector Number Reduction: Survey and Experimental Evaluations
Author
정호기
Keywords
Reduced-set method; support vector machine (SVM); support vector number reduction (SVNR)
Issue Date
2014-04
Publisher
IEEE
Citation
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS; APR 2014, 15, 2, p463-p476
Abstract
Although a support vector machine (SVM) is one of the most frequently used classifiers in the field of intelligent transportation systems and shows competitive performances in various problems, it has the disadvantage of requiring relatively large computations in the testing phase. To make up for this weakness, diverse methods have been researched to reduce the number of support vectors determining the computations in the testing phase. This paper is intended to help engineers using the SVM to easily apply support vector number reduction to their own particular problems by providing a state-of-the-art survey and quantitatively comparing three implementations belonging to postpruning, which exploits the result of a standard SVM. In particular, this paper confirms that the support vector number of a pedestrian classifier using a histogram-of-oriented-gradient-based feature and a radial-basis-function-kernel-based SVM can be reduced by more than 99.5% without any accuracy degradation using iterative preimage addition, which can be downloaded from the Internet.
URI
http://ieeexplore.ieee.org/abstract/document/6623200/http://hdl.handle.net/20.500.11754/51051
ISSN
1524-9050
DOI
10.1109/TITS.2013.2282635
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > AUTOMOTIVE ENGINEERING(미래자동차공학과) > Articles
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