Paper abstract

Large Margin vs. Large Volume in Transductive

Ran El-Yaniv - Technion - Israel Institute of Technology, Israel
Dmitry Pechyony - Technion - Israel Institute of Technology, Israel
Vladimir Vapnik - NEC Laboratories America, Inc., USA

Session: Semi Supervised Learning
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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.