MATEC Web Conf.
Volume 128, 20172017 International Conference on Electronic Information Technology and Computer Engineering (EITCE 2017)
|Number of page(s)||4|
|Section||Simulation Model and Algorithm|
|Published online||25 October 2017|
An Updated Projection Twin Support Vector Machine for Classification
School of Information and Engineering, Yancheng Institute of Technology, 224001 Yancheng, China
a Corresponding author: firstname.lastname@example.org
Based on projection twin support vector machine (PTSVM) and its extensions, this paper describes an updated PTSVM (UPTSVM) for classification. Compared with existing PTSVMs, UPTSVM has its own advantages. First, similar to the standard support vector machine (SVM), UPTSVM maintains the consistency of the optimization problems in the linear and nonlinear case, which results in the nonlinear formulations can be directly turned into the linear ones. Nevertheless, the existing PTSVMs lose the consistency because of using empirical kernel to construct nonlinear formulations. Second, UPTSVM avoids the inverse of kernel matrixes in the course of solving dual problems, which indicates it can not only reduce computing time but also save storage space. Third, UPTSVM can be practically proved equivalent to the PTSVM with regularization (RPTSVM). Experimental results on lots of data sets show the virtue of the presented method.
© The authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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