DocumentCode :
2194633
Title :
Subspace Distance-Based Sampling Method for SVM
Author :
Zhou, Xiaofei ; Shi, Yong
Author_Institution :
Grad. Univ. of Chinese Acad. of Sci., Beijing, China
fYear :
2010
fDate :
13-13 Dec. 2010
Firstpage :
1289
Lastpage :
1296
Abstract :
Support Vector Machine (SVM) is an effective classifier for classification task, but a vital shortcoming of SVM is that it needs huge computation for large scale learning tasks. Sample selection is a feasible strategy to overcome the problem. In order to rapidly reduce training samples without sacrificing recognition accuracy, this paper presents a novel sample selection strategy based on subspace distance, called subspace sample selection. Subspace selection method tries to select boundary samples of each class convex hull by iteratively absorbing the furthest sample to the subspace of chosen samples. This selection method can efficiently represent original training set and support SVM classification. Experimental results also show that our sample selection method can select fewer high quality samples to maintain the recognition accuracy of SVM.
Keywords :
iterative methods; pattern classification; support vector machines; SVM classification; boundary samples; class convex hull; classification task; recognition accuracy; sample selection strategy; subspace distance; subspace sample selection; support vector machine; training set; Classification; Kernel; SVM; Sample selection; Subspace;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshops (ICDMW), 2010 IEEE International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-9244-2
Electronic_ISBN :
978-0-7695-4257-7
Type :
conf
DOI :
10.1109/ICDMW.2010.84
Filename :
5693442
Link To Document :
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