Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 16, Issue 3, Pages 774-776Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2005.844857
Keywords
support vector machines (SVMs); sequential minimal optimization (SMO) algorithm; convergence; termination
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Sequential minimal optimization (SMO) algorithm is one of the simplest decomposition methods for learning of support vector machines (SVMs). Keerthi and Gilbert have recently studied the convergence property of SMO algorithm and given a proof that SMO algorithm always stops within a finite number of iterations. In this letter, we point out the incompleteness of their proof and give a more rigorous proof.
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