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16

NIPS
1998

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Using Analytic QP and Sparseness to Speed Training of Support Vector Machines

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Using Analytic QP and Sparseness to Speed Training of Support Vector Machines

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Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) problem. This paper proposes an algorithm for training SVMs: Sequential Minimal Optimization, or SMO. SMO breaks the large QP problem into a series of smallest possible QP problems which are analytically solvable. Thus, SMO does not require a numerical QP library. SMO's computation time is dominated by evaluation of the kernel, hence kernel optimizations substantially quicken

John C. Platt

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Added	01 Nov 2010
Updated	01 Nov 2010
Type	Conference
Year	1998
Where	NIPS
Authors	John C. Platt

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