Large margin vs. large volume in transductive learning

Authors:
Ran El-Yaniv;Dmitry Pechyony;Vladimir Vapnik
Affiliations:
Computer Science Department, Technion-Israel Institute of Technology, Haifa, Israel 32000;Computer Science Department, Technion-Israel Institute of Technology, Haifa, Israel 32000;NEC Laboratories America, Princeton, USA 08540
Venue:
Machine Learning
Year:
2008

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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.