A kernel-free quadratic surface support vector machine for semi-supervised learning

A kernel-free quadratic surface support vector machine for semi-supervised learning

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Article ID: iaor20162702
Volume: 67
Issue: 7
Start Page Number: 1001
End Page Number: 1011
Publication Date: Jul 2016
Journal: Journal of the Operational Research Society
Authors: , , ,
Keywords: statistics: regression
Abstract:

In this paper, we propose a kernel‐free semi‐supervised quadratic surface support vector machine model for binary classification. The model is formulated as a mixed‐integer programming problem, which is equivalent to a non‐convex optimization problem with absolute‐value constraints. Using the relaxation techniques, we derive a semi‐definite programming problem for semi‐supervised learning. By solving this problem, the proposed model is tested on some artificial and public benchmark data sets. Preliminary computational results indicate that the proposed method outperforms some existing well‐known methods for solving semi‐supervised support vector machine with a Gaussian kernel in terms of classification accuracy.

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