PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Multi-kernel Framework for Inductive Semi-supervised Learning
Xilan Tian, Gilles Gasso and Stéphane Canu
In: ESANN 2011, 27 - 29 April 2011, Bruges, Belgium.

Abstract

We investigate the benet of combining both cluster assump- tion and manifold assumption underlying most of the semi-supervised al- gorithms using the exibility and the eciency of multi-kernel learning. The multiple kernel version of Transductive SVM (a cluster assumption based approach) is proposed and it is solved based on DC (Dierence of Convex functions) programming. Promising results on benchmark data sets suggesting the eectiveness of proposed work.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:7573
Deposited By:Gilles Gasso
Deposited On:17 March 2011