Wednesday, October 21, 2009
04:30 PM in REC 315
Assistant Professor Jian Zhang
Department of Statistics, Purdue University

An Introduction to Statistical Learning Theory

Abstract

The goal of statistical learning theory is to study the statistical properties of learning algorithms. In particular, most results are in the form of generalization error bounds, from which consistency and convergence rates might be derived. I will first introduce the basic techniques and concepts and then show how to develop both data-independent and data-dependent generalization error bounds of learning algorithms. The results will also be extended to study regression problems. In the later part of the talk I will give a brief overview of my recent research on statistical machine learning, in particular some specialized learning problems and their applications.