---------------------------------------------------------------------- ARTIFICIAL INTELLIGENCE RESEARCH SEMINAR Department of Computer Science Worcester Polytechnic Institute SPEAKER: James Kilian Machine Vision Laboratory, ECE TITLE: "Introduction to Support Vector Learning" DATE: Thursday, Dec. 2, 1999 TIME: 11 am ROOM: FL246, Beckett Conference Room. ABSTRACT: The support vector machine (SVM) is a relatively new technique for solving a variety of learning and function approximation problems. Rather than training to minimize empirical risk, as is often done with neural networks and radial basis function networks, SVM minimize an upper bound on the generalization error -- a special case of an inductive principle called structural risk minimization (also referred to as Vapnik-Chervonenkis (VC) theory). The pragmatic basis of support vector learning is a nonlinear mapping of a learning problem to a high dimensional reproducing kernel Hilbert space followed by a linear learning step (e.g., classification, regression, approximation). This talk summarizes the content of an independent study course, conducted this semester, on statistical learning theory and support vector machines. The underlying theory of SVM will be reviewed, followed by discussions of the support vector learning procedure, comparisons with other techniques, and an application to a synthetic classification problem. The talk will conclude with open questions on SVM raised in the course of the independent study course. ---------------------------------------------------------------------- Everyone is welcome to attend!