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

• Type: Conference
• Year: 1998
• Where: NIPS
• Authors: John C. Platt
• Keywords: Large Qp Problem, NIPS 1998, NIPS 2007, Qp Problems, Smallest Possible Qp