DocumentCode :
2111826
Title :
Efficient Implementation of Nonparallel Hyperplanes Classifier
Author :
Xu Chan Ju ; Ying Jie Tian
Author_Institution :
Acad. of Math. & Syst. Sci., Beijing, China
Volume :
3
fYear :
2012
fDate :
4-7 Dec. 2012
Firstpage :
5
Lastpage :
9
Abstract :
In this paper, we proposed a novel nonparallel hyper planes classifier for binary classification, termed as NHC. Though this method can be in fact proved equivalent to an improved twin support vector machine (TWSVM), it has the incomparable advantages than existing TWSVMs. First, the optimization problems in NHC can be solved efficiently by successive over relaxation (SOR) without needing to compute the large inverse matrices before training as TWSVMs usually do, Second, kernel trick can be applied directly to NHC, which is superior to existing TWSVMs. Experimental results on lots of data sets show the efficiency of our method in both computation time and classification accuracy.
Keywords :
matrix algebra; optimisation; parallel processing; pattern classification; support vector machines; NHC; SOR; TWSVM; binary classification; classification accuracy; computation time; improved twin support vector machine; inverse matrices; kernel trick; nonparallel hyperplanes classifier; optimization problems; successive overrelaxation; inverse matrices; kernel trick; successive overrelaxation; support vector machine; twin support vector machine(TWSVM);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Web Intelligence and Intelligent Agent Technology (WI-IAT), 2012 IEEE/WIC/ACM International Conferences on
Conference_Location :
Macau
Print_ISBN :
978-1-4673-6057-9
Type :
conf
DOI :
10.1109/WI-IAT.2012.30
Filename :
6511638
Link To Document :
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