Massive Data Classification via Unconstrained Support Vector Machines

O. L. Mangasarian and M. E. Thompson
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O. L. Mangasarian: University of Wisconsin
M. E. Thompson: University of Wisconsin

Journal of Optimization Theory and Applications, 2006, vol. 131, issue 3, No 1, 315-325

Abstract: Abstract A highly accurate algorithm, based on support vector machines formulated as linear programs (Refs. 1–2), is proposed here as a completely unconstrained minimization problem (Ref. 3). Combined with a chunking procedure (Ref. 4), this approach, which requires nothing more complex than a linear equation solver, leads to a simple and accurate method for classifying million-point datasets. Because a 1-norm support vector machine underlies the proposed approach, the method suppresses input space features as well. A state-of-the-art linear programming package (CPLEX, Ref. 5) fails to solve problems handled by the proposed algorithm.

Keywords: Data classification; support vector machines; linear programming; unconstrained minimization; Newton method (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10957-006-9157-x

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