an interview with Prof. Vladimir Vapnik(采访模式识别专家).pdfVIP

Learning Has Just Started - an interview with Prof. Vladimir Vapnik Contributed by Ran Gilad-Bachrach Sunday, 02 March 2008 00:00 - Last Updated Wednesday, 09 April 2008 As a part of the renovation of the learningTheory.org web site, we are launching a series of interviews with leading researchers in learning theory and related fields. We are proud that Prof. Vladimir Vapnik accepted our invitation to be the first to be interviewed.     Prof. Vapnik has been working on learning theory related problems for more than four decades. Together with Alexey Chervonenkis he studied the problem of uniform convergence of empirical means and developed the VC theory. He also developed the large margin principles and the Support Vector Machines algorithm. R-GB: Thank you for accepting our invitation to be the first one to be interviewed for learningtheory.org. Can you tell us what your current research directions are? V-V: My current research interest is to develop advanced models of empirical inference. I think that the problem of machine learning is not just a technical problem. It is a general problem of philosophy of empirical inference. One of the ways for inference is induction. The main philosophy of inference developed in the past strongly connected the empirical inference to the inductive learning process.     I believe that induction is a rather restrictive model of learning and I am trying to develop more advanced models. First, I am trying to develop non-inductive methods of inference, such as transductive inference, selective inference, and many other options. Second, I am trying to introduce non-classical ways of inference. Here is an example of such an inference. In the classical scheme, given a set of admissible indicator functions {f(x)} and given a set of training data, pairs (x,y) X { , one tries to find the best classification function in this set. In the new i i Î ´ ±1} setting, called master-class lea