Large Margin vs. Large Volume in Transductive Learning
Ran El-Yaniv, Dmitry Pechyony and Vladimir Vapnik
Machine Learning Journal
ISSN 0885-6125 (Print) 1573-0565 (Online)
We consider a large volume principle for transductive learn-
ing 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.