Abstract
A new simple and linearly convergent scheme is proposed in this paper for the dual formulation of twin parametric-margin support vector machine. Here, instead of considering the 1-norm error of slack variables, we have considered 2-norm of the vector of slack variables to make the objective functions strongly convex. Further, the proposed method solves a pair of linearly convergent iterative schemes instead of solving a pair of quadratic programming problems as in case of twin support vector machine and twin parametric-margin support vector machine. The proposed method considers in finding two parametric-margin hyperplanes that makes it less sensitive to heteroscedastic noise structure. Our experiments, performed on synthetic and real-world datasets, conclude that the proposed method has comparable generalization performance and improved learning speed in comparison to twin support vector machine, Lagrangian twin support vector machine and twin parametric-margin support vector machine.
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Borah, P., Gupta, D. (2018). On Lagrangian Twin Parametric-Margin Support Vector Machine. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-10-8657-1_36
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DOI: https://doi.org/10.1007/978-981-10-8657-1_36
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