PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Synthesis of Maximum Margin and Multiview Learning using Unlabeled Data
Sandor Szedmak and John Shawe-Taylor
In: ESANN 2006, 26-28 April 2006, Bruges, Belgium.

Abstract

In this presentation we show the semi-supervised learning with two input sources can be transformed into a maximum margin problem to be similar to a binary SVM. Our formulation exploits the unlabeled data to reduce the complexity of the class of the learning functions. In order to measure how the complexity is decreased we use the Rademacher Complexity Theory. The corresponding optimization problem is convex and it is efficiently solvable for large-scale applications as well.

EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2080
Deposited By:Sandor Szedmak
Deposited On:17 February 2006