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2008
ACM

Large margin vs. large volume in transductive learning

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Large margin vs. large volume in transductive learning
Abstract. We consider a large volume principle for transductive learning that prioritizes the transductive equivalence classes according to the volume they occupy in hypothesis space. We approximate volume maximization using a geometric interpretation of the hypothesis space. The resulting algorithm is defined via a non-convex optimization problem that can still be solved exactly and efficiently. We provide a bound on the test error of the algorithm and compare it to transductive SVM (TSVM) using 31 datasets.
Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2008
Where ML
Authors Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik
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