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NIPS
1998

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