Statistical Learning: Overview and Applications
Friday November 1, 2002
Host: Davi Geiger, email@example.com, 212-998-3235
Host: Demetri Terzopoulos, firstname.lastname@example.org, 212-998-3477
I will give a brief overview of our recent work on statistical learning theory, including results on the problem of classification and function approximation. I will describe applications in various domains -- such as visual recognition, computer graphics and bioinformatics.
Some relevant papers (the papers can be downloaded from http://www.ai.mit.edu/projects/cbcl/publications/all-year.html):
Evgeniou, T., M. Pontil and T. Poggio. Regularization Networks and Support Vector Machines, Advances in Computational Mathematics, 13, 1, 1-50, 2000.
Heisele, B., T. Serre, M. Pontil, T. Vetter and T. Poggio. Categorization by Learning and Combining Object Parts. In: Advances in Neural Information Processing Systems (NIPS'01), Vancouver, Canada, 2002, to appear.
Ezzat, T. Geiger D. and Poggio T. Trainable Videorealistic Speech Animation. SigGraph, 2002.
Pomeroy, S.L., P. Tamayo, M. Gaasenbeek, L.M. Sturia, M. Angelo, M.E. McLaughlin, J.Y.H. Kim, L.C. Goumnerova, P. M. Black, C. Lau, J.C. Allen, D. Zagzag, M.M. Olson, T. Curran, C. Wetmore, J.A. Biegel, T. Poggio, S. Mukherjee, R. Rifkin, A. Califano, G. Stolovitzky, D. N. Louis, J.P. Mesirov, E.S. Lander and T.R. Golub. Prediction of Central Nervous System Embryonal Tumour Outcome Based on Gene Expression, Nature, Vol. 415, 436-442, January 2002.