Prof. Junhui Wang : Probability Estimation for Large Margin Classifiers

Posted by , part of the Statistics and Data Science Seminar.

At

Oct. 8, 2008, 4:15 p.m.

In

SEO 612

Abstract

Large margin classifiers have proven to be effective in delivering high predictive accuracy, particularly those focusing on the decision boundaries and bypassing the requirement of estimating the class probability given input for discrimination. As a result, these classifiers may not directly yield an estimated class probability, which is of interest itself in many real applications. In this talk, I will present a novel method to estimate the class probability through sequential weighted classifications, by utilizing features of interval estimation of large margin classifiers. In particular, I will discuss four aspects: (1) the idea and methodology development; (2) tuning parameter selection; (3) regularization solution path; (4) a statistical learning theory. Numerical examples will be provided to demonstrate the advantage of our proposed methodology.