Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19359
Title: A SVM based classification method for homogeneous data
Authors: Li, H
Chung, FL 
Wang, S
Keywords: Homogeneous data
Multi-observation samples
SVM classification
Issue Date: 2015
Publisher: Elsevier
Source: Applied soft computing, 2015, v. 36, p. 228-235 How to cite?
Journal: Applied soft computing 
Abstract: A novel classification method based on SVM is proposed for binary classification tasks of homogeneous data in this paper. The proposed method can effectively predict the binary labeling of the sequence of observation samples in the test set by using the following procedure: we first make different assumptions about the class labeling of this sequence, then we utilize SVM to obtain two classification errors respectively for each assumption, and finally the binary labeling is determined by comparing the obtained two classification errors. The proposed method leverages the homogeneity within the same classes and exploits the difference between different classes, and hence can achieve the effective classification for homogeneous data. Experimental results indicate the power of the proposed method. ? 2015 Elsevier B.V. All rights reserved.
URI: http://hdl.handle.net/10397/19359
ISSN: 1568-4946
EISSN: 1872-9681
DOI: 10.1016/j.asoc.2015.07.027
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