A simple proof of the convergence of the SMO algorithm for linearly separable problems
J López, JR Dorronsoro - Artificial Neural Networks–ICANN 2009: 19th …, 2009 - Springer
Artificial Neural Networks–ICANN 2009: 19th International Conference, Limassol …, 2009•Springer
We give a new proof of the convergence of the SMO algorithm for SVM training over linearly
separable problems that partly builds on the one by Mitchell et al. for the convergence of the
MDM algorithm to find the point of a convex set closest to the origin. Our proof relies in a
simple derivation of SMO that we also present here and, while less general, it is
considerably simpler than previous ones and yields algorithmic insights into the working of
SMO.
separable problems that partly builds on the one by Mitchell et al. for the convergence of the
MDM algorithm to find the point of a convex set closest to the origin. Our proof relies in a
simple derivation of SMO that we also present here and, while less general, it is
considerably simpler than previous ones and yields algorithmic insights into the working of
SMO.
Abstract
We give a new proof of the convergence of the SMO algorithm for SVM training over linearly separable problems that partly builds on the one by Mitchell et al. for the convergence of the MDM algorithm to find the point of a convex set closest to the origin. Our proof relies in a simple derivation of SMO that we also present here and, while less general, it is considerably simpler than previous ones and yields algorithmic insights into the working of SMO.
Springer
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