An efficient algorithm for solving linearized SVM regression problems

Dongdong Lei, Andrew Crampton, John C Mason, C Ross

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Support vector machines (SVMs) for regression are most commonly formulated as quadratic optimization problems (QP), and can be solved by several specialized algorithms, which need time and memory resources of the order of m 2 . J. Weston at al. [Support vector density estimation, In Advances in Kernel Methods-Support Vector Learning, B. Schölkopf, C. J. C. Burges, and A. J. Smola (eds.), MIT Press, Cambridge, MA, US, 293–306 (1999)] suggested that this problem could be reduced to a linear programming problem by adopting a linearized regularization term. Although the standard simlex approach can be applied to solve it, it would be very desirable to have a purpose-built algorithm. In this paper, we present an algorithm which exploits the special features of such linearized SVM problems and solves them in a very efficient way. The savings on both computational effort and storage requirement are significant, and are illustrated by several sets of experimental results.
Original languageEnglish
Title of host publicationCurve and Surface Fitting
Subtitle of host publicationSaint-Malo 2002
EditorsAlbert Cohen, Jean-Louis Merrien, Larry L. Schumaker
PublisherNashboro Press
Number of pages10
ISBN (Print)9780972848213, 0972848215
Publication statusPublished - 1 Jun 2003
Event5th International Conference on Curves and Surfaces: Curve and Surface Fitting 2002 - Saint Malo, France
Duration: 27 Jun 20023 Jul 2002
Conference number: 5


Conference5th International Conference on Curves and Surfaces
CitySaint Malo
Internet address


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