Rigorous proof of termination of SMO algorithm for support vector Machines

Authors:
N. Takahashi;T. Nishi
Affiliations:
Dept. of Comput. Sci. & Commun. Eng., Kyushu Univ., Fukuoka, Japan;-
Venue:
IEEE Transactions on Neural Networks
Year:
2005

Citing 0
Cited 5

Maximum-Gain Working Set Selection for SVMs

The Journal of Machine Learning Research
Fast support vector data descriptions for novelty detection

IEEE Transactions on Neural Networks
Fast training of SVM via morphological clustering for color image segmentation

ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Scaling up minimum enclosing ball with total soft margin for training on large datasets

Neural Networks
VLSI design of an SVM learning core on sequential minimal optimization algorithm

IEEE Transactions on Very Large Scale Integration (VLSI) Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.