Speaker: Dr. Jing Peng

Title: Finite Sample Error Bound for Parzen Windows

Date and Time: Friday, September 30, 2005, 11:30 am
Place: 145 Coates

Directions: http://www.lsu.edu/campus/maps/COAT01.html

Abstract:

Parzen Windows as a nonparametric method has been applied to a variety of density estimation as well as classification problems. Similar to nearest neighbor methods, Parzen Windows does not involve learning. While it converges to true but unknown probability densities in the asymptotic limit, there is a lack of theoretical analysis on its performance with finite samples. In this  talk we establish a finite sample error bound for Parzen Windows. We first show that Parzen Windows is an approximation to regularized least squares (RLS) methods that have been well studied in statistical learning theory. We then derive the finite sample error bound for Parzen Windows, and discuss the properties of the error bound and its relationship to the error bound for RLS. This analysis provides interesting insight into local averaging techniques from the point of view of learning theory. Finally, we provide empirical results on the performance of Parzen Windows and other methods such as nearest neighbors, RLS and SVMs on a number of real data sets. These results corroborate well our theoretical analysis.

Short Bio:

Jing Peng has been an assistant professor of computer science at Tulane University since 2001. From 1999 to 2001 he was on the faculty of computer science at Oklahoma State University. He was a research scientist at University of California at Riverside from 1997 to 1999. His research interest is in the areas of machine learning and content-based image retrieval. He has over 80 publications in his chosen field of research.