Rigorous proof of termination of SMO algorithm for support vector Machines

Author

Takahashi, Naoyuki ; Nishi, Tomoki

Author_Institution

Dept. of Comput. Sci. & Commun. Eng., Kyushu Univ., Fukuoka, Japan

Volume

16

Issue

3

fYear

2005

fDate

5/1/2005 12:00:00 AM

Firstpage

774

Lastpage

776

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.

Keywords

convergence; optimisation; support vector machines; convergence; rigorous proof; sequential minimal optimization; support vector machine; Algorithm design and analysis; Convergence; Machine learning; Machine learning algorithms; Matrix decomposition; Neural networks; Optimization methods; Pattern recognition; Quadratic programming; Support vector machines; Support vector machines (SVMs); convergence; sequential minimal optimization (SMO) algorithm; termination; Algorithms; Artificial Intelligence; Cluster Analysis; Computer Simulation; Computing Methodologies; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2005.844857

Filename

1427778