Kernel classification using a linear programming approach
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A support vector machine (SVM) classifier corresponds in its most basic form to a quadratic programming problem. Various linear variations of support vector classification have been investigated such as minimizing the L1-norm of the weight-vector instead of the L2-norm. In this paper we introduce a classifier where we minimize the boundary (lower envelope) of the epigraph that is generated over a set of functions, which can be interpreted as a measure of distance or slack from the origin. The resulting classifier appears to provide a generalization performance similar to SVMs while displaying a more advantageous computational complexity. The discussed formulation can also be extended to allow for cases with imbalanced data.
KeywordsKernel methods Classification Linear programming
Mathematics Subject Classification (2010)68T05 90C05
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Theodore Trafalis work has been conducted at the National Research Institute University Higher School of Economics and has been supported by the RSF grant n. 14-41-00039.
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