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March 15, Tuesday
12:00 – 14:00

In recent years the notion of learning real-valued functions by maximizing the sample-margin has been shown to improve the learning error rates. This has been the primary cause behind the success of the latest generation of machine learning algorithms such as Support Vector Machines. As in other forms of statistical model selection the generalization accuracy is improved as a result of having a reduced effective hypothesis-class complexity (epsilon-entropy).

When learning binary-valued hypotheses the complexity that governs the error convergence rate is the Vapnik and Chervonenkis growth function. In this talk I describe my recent results on the reduced effective complexity of classes of binary-valued functions which have a large sample margin. It turns out that such classes exhibit a strong combinatorial threshold growth with respect to the margin value.

My result can be viewed as an extension of the well known Sauer's Lemma.

Bio:

B.S. and M.S., Electrical Engineering, Brooklyn Polytechnic Institute of New York

Ph.D. Moore School of Electrical Engineering, University of Pennsylvania

Post-Doctoral, Electrical Engineering, Technion

Lecturer at Computer Science Department, University College London

Currently Lecturer at Industrial Engineering, Ben Gurion University, Israel