A multiple kernel framework for inductive semi-supervised SVM learning

Authors:
Xilan Tian;Gilles Gasso;Stéphane Canu
Affiliations:
LITIS EA-4108, INSA de Rouen, Avenue de l'Université, 76801 St Etienne du Rouvray, France;LITIS EA-4108, INSA de Rouen, Avenue de l'Université, 76801 St Etienne du Rouvray, France;LITIS EA-4108, INSA de Rouen, Avenue de l'Université, 76801 St Etienne du Rouvray, France
Venue:
Neurocomputing
Year:
2012

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Abstract

We investigate the benefit of combining both cluster assumption and manifold assumption underlying most of the semi-supervised algorithms using the flexibility and the efficiency of multiple kernel learning. The multiple kernel version of Transductive SVM (a cluster assumption based approach) is proposed and it is solved based on DC (Difference of Convex functions) programming. Promising results on benchmark data sets and the BCI data analysis suggest and support the effectiveness of proposed work.