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.

• Type: Journal
• Year: 2008
• Where: ML
• Authors: Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik
• Keywords: Large Volume Principle, Machine Learning, ML 2008, Non-convex Optimization Problem, Transductive Equivalence Classes