Rigorous proof of termination of SMO algorithm for support vector machines

Norikazu Takahashi; Tetsuo Nishi

doi:10.1109/TNN.2005.844857

Rigorous proof of termination of SMO algorithm for support vector machines

Norikazu Takahashi, Tetsuo Nishi

Research output: Contribution to journal › Article › peer-review

37 Citations (Scopus)

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.

Original language	English
Pages (from-to)	774-776
Number of pages	3
Journal	IEEE Transactions on Neural Networks
Volume	16
Issue number	3
DOIs	https://doi.org/10.1109/TNN.2005.844857
Publication status	Published - May 2005
Externally published	Yes

Keywords

Convergence
Sequential minimal optimization (SMO) algorithm
Support vector machines (SVMs)
Termination

ASJC Scopus subject areas

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

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AB - 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.

KW - Convergence

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KW - Support vector machines (SVMs)

KW - Termination

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Rigorous proof of termination of SMO algorithm for support vector machines

Abstract

Keywords

ASJC Scopus subject areas

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