PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semi-Supervised Classification by Low Density Separation
Olivier Chapelle and Alexander Zien
In: AI STATS 2005(2004).

Abstract

We believe that the cluster assumption is key to successful semi-supervised learning. Based on this, we propose three semi-supervised algorithms: 1. deriving graph-based distances that emphazise low density regions between clusters, followed by training a standard SVM; 2. optimizing the Transductive SVM objective function, which places the decision boundary in low density regions, by gradient descent; 3. combining the first two to make maximum use of the cluster assumption. We compare with state of the art algorithms and demonstrate superior accuracy for the latter two methods.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:388
Deposited By:Olivier Chapelle
Deposited On:18 December 2004