HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Keerthi, S.S. Articles by Shevade, S.K. Search for Related Content PubMed PubMed Citation Articles by Keerthi, S.S. Articles by Shevade, S.K.

SMO Algorithm for Least-Squares SVM Formulations

mpessk{at}guppy.mpe.nus.edu.sg, Department of Mechanical Engineering, National University of Singapore, Singapore 117576

S.K. Shevade

gissk{at}nus.edu.sg, Genome Institute of Singapore, National University of Singapore, Singapore 117528

This article extends the well-known SMO algorithm of support vector machines (SVMs) to least-squares SVM formulations that include LS-SVM classification, kernel ridge regression, and a particular form of regularized kernel Fisher discriminant. The algorithm is shown to be asymptotically convergent. It is also extremely easy to implement. Computational experiments show that the algorithm is fast and scales efficiently (quadratically) as a function of the number of examples.

This article has been cited by other articles:

				T. Knebel, S. Hochreiter, and K. Obermayer An SMO Algorithm for the Potential Support VectorMachine Neural Comput., January 1, 2007; 20(1): 271 - 287. [Abstract] [Full Text] [PDF]