NDLI: Simple Proof of Convergence of the SMO Algorithm for Different SVM Variants

Content Provider	IEEE Xplore Digital Library
Author	Lopez, J. Dorronsoro, J.R.
Copyright Year	2012
Abstract	In this brief, we give a new proof of the asymptotic convergence of the sequential minimum optimization (SMO) algorithm for both the most violating pair and second order rules to select the pair of coefficients to be updated. The proof is more self-contained, shorter, and simpler than previous ones and has a different flavor, partially building upon Gilbert's original convergence proof of its algorithm to solve the minimum norm problem for convex hulls. It is valid for both support vector classification (SVC) and support vector regression, which are formulated under a general problem that encompasses them. Moreover, this general problem can be further extended to also cover other support vector machines (SVM)-related problems such as -SVC or one-class SVMs, while the convergence proof of the slight variant of SMO needed for them remains basically unchanged.
Page Count	6
File Size	200016
Starting Page	1142
Ending Page	1147
File Format	PDF
ISSN	2162237X
Volume Number	23
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Convergence Static VAr compensators Support vector machines Vectors Learning systems Indexes Convex functions support vector machines Classification decomposition methods regression sequential minimum optimization (SMO)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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