PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semi-Supervised Support Vector Machines and Application to Spam Filtering
Alexander Zien
In: ECML 2006 Discovery Challenge, 22 September 2006, Berlin, Germany.

Abstract

After introducing the semi-supervised support vector machine (aka TSVM for "transductive SVM"), a few popular training strategies are briefly presented. Then the assumptions underlying semi-supervised learning are reviewed. Finally, two modern TSVM optimization techniques are applied to the spam filtering data sets of the workshop; it is shown that they can achieve excellent results, if the problem of the data being non-iid can be handled properly.

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EPrint Type:Conference or Workshop Item (Invited Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:3098
Deposited By:Alexander Zien
Deposited On:19 December 2007