Abstract
This paper proposes a new algorithm for training support vector machines: Sequential
Minimal Optimization, or SMO. Training a support vector machine requires the solution of
a very large quadratic programming (QP) optimization problem. SMO breaks this large
QP problem into a series of smallest possible QP problems. These small QP problems are
solved analytically, which avoids using a time-consuming numerical QP optimization as an
inner loop. The amount of memory required for SMO is linear in...
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