An Approach for Incremental Semi-supervised SVM

I
ICDMW
2007
Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)
Abstract - An Approach for Incremental Semi-supervised SVM

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Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)

Omaha, Nebraska, USA

October 28-October 31

ISBN: 0-7695-3033-8

	ASCII Text	x
	Wael Emara, Mehmed Kantardzic Marcel Karnstedt, Kai-Uwe Sattler, Dirk Habich, Wolfgang Lehner, "An Approach for Incremental Semi-supervised SVM," Data Mining Workshops, International Conference on, pp. 539-544, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007), 2007.

	BibTex	x
	@article{ 10.1109/ICDMW.2007.106, author = {Wael Emara and Mehmed Kantardzic Marcel Karnstedt and Kai-Uwe Sattler and Dirk Habich and Wolfgang Lehner}, title = {An Approach for Incremental Semi-supervised SVM}, journal ={Data Mining Workshops, International Conference on}, volume = {0}, year = {2007}, isbn = {0-7695-3033-8}, pages = {539-544}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDMW.2007.106}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining Workshops, International Conference on TI - An Approach for Incremental Semi-supervised SVM SN - 0-7695-3033-8 SP539 EP544 A1 - Wael Emara, A1 - Mehmed Kantardzic Marcel Karnstedt, A1 - Kai-Uwe Sattler, A1 - Dirk Habich, A1 - Wolfgang Lehner, PY - 2007 VL - 0 JA - Data Mining Workshops, International Conference on ER -

Wael Emara

Mehmed Kantardzic Marcel Karnstedt

Kai-Uwe Sattler

Dirk Habich

Wolfgang Lehner

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2007.106

In this paper we propose an approach for incremental learning of semi-supervised SVM. The proposed approach makes use of the locality of radial basis function kernels to do local and incremental training of semi-supervised support vector machines. The algorithm introduces a se- quential minimal optimization based implementation of the branch and bound technique for training semi-supervised SVM problems. The novelty of our approach lies in the

Citation:

Wael Emara, Mehmed Kantardzic Marcel Karnstedt, Kai-Uwe Sattler, Dirk Habich, Wolfgang Lehner, "An Approach for Incremental Semi-supervised SVM," icdmw, pp.539-544, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007), 2007

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