The Weizmann Institute of Science Faculty of Mathematics and Computer Science Vision and Robotics Seminar Shai Avidan Interdisciplinary Center will speak on Subset Selection for Computationally Efficient SVM Classification Abstract: We update the {\bf SVM} score of an object through a video sequence with a small and variable subset of support vectors. In the first frame we use all the support vectors to compute the {\bf SVM} score of the object but in subsequent frames we use only a small and variable subset of support vectors to update the {\bf SVM} score. In each frame we calculate the dot-products of the support vectors in the subset with the pattern of the object in the current frame. The difference in the dot-products, between past and current frames, is used to update the {\bf SVM} score. This is done at a fraction of the computational cost required to re-evaluate the {\bf SVM} score from scratch in every frame. The two methods we develop are ``loop unrolling", in which we break the set of all support vectors into subsets of equal size and use them cyclically, and ``Maximum Variance Subset Selection'', in which we choose the support vectors whose dot-product with the test pattern varied the most in previous frames. We combine this techniques together for the problem of maintaining the {\bf SVM} score of objects through a video sequence. Results on real video sequences are shown. The lecture will take place in the Lecture Hall, Room 1, Ziskind Building on Thursday, January 9, 2003 at 11:00