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OneClassMaxMinOver

Fabian Timm, Kai Labusch, Thomas Martinetz

 

OneClassMaxMinOver  We introduce the OneClassMaxMinOver (OMMO) algorithm for the problem of one-class support vector classication. The algorithm is extremely simple and therefore a convenient choice for practitioners. We prove that in the hard-margin case the algorithm converges with O(1/sqrt(t)) to the maximum margin solution of the support vector approach for one-class classification introduced by Schölkopf et al. Furthermore, we propose a 2-norm soft margin generalisation of the algorithm and apply the algorithm to articial datasets and to the real world problem of face detection in images. We obtain the same performance as sophisticated SVM software such as libSVM.

For source code (Matlab, C++) or further information please send an email to Fabian Timm (timm(AT)inb(DOT)uni-luebeck(DOT)de).

You can download the paper here.

If you use anything of the paper or software please cite:

Kai Labusch, Fabian Timm, and Thomas Martinetz. Simple incremental one-class Support Vector classification. In Gerhard Rigoll, editor, Pattern Recognition - Proceedings of the DAGM, Lecture Notes in Computer Science, pages 21-30, 2008.

 

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