University of Houston
Department of Computer Science
In partial fulfillment of the Requirements for the Degree of
Master of Science


Puneet Sarda
will defend his thesis

Signal Enhancement using Multivariate Classification Techniques and Physical Constraints

Abstract

We report on an empirical comparison of several multivariate classification techniques (e.g. Random Forest, Bayesian Classification, Multilayer Perceptrons) for signal identification and enhancement; our experiments use K^* dataset as a test case. We show 1) how information about physical constraints obtained from kinematic fitting procedures can be used to enrich the original feature representation and 2) the effect of using cost matrices in generalization performance. The former step is done through a derivation of L particle parameters (e.g. momentum, energy and mass) using kinematic fitting; the degree of fit using X² statistic is used as a new feature. Overall, our goal is to improve classifier performance using kinematic fitting and cost sensitive classification.

Date: Monday, November 7 ^th, 2005
Time: 4:30 PM
Place: 550-PGH



Faculty, students, and the general public are invited.
Thesis Advisor: Dr. Ricardo Vilalta