Security Lab Seminar

Wed., Aug. 20, 12:00 p.m.

Speaker: Na Tang

 

Title: Solving Inverse Problems via Machine Learning and Knowledge Discovery

Abstract: Automated web-based knowledge acquisition can play a useful role in developing systematic methods for solving inverse problems from sparse and unreliable data sets. As inverse problems are ill-posed, they are normally solved by using some sort of regularization procedure - a mathematical strategy that seeks to supply the "missing data." We seek to fill the missing data entries by a judicious search of the WWW. The next step is to learn the structure and parameters of the unknown system. The task of learning the structure can be accomplished either by an automated evolutionary search or by a user-assisted generate-and-test strategy. In either case, the goal is to learn a Bayesian network structure by looking at various possible node orderings and interconnection topologies. The parameters to be estimated are conditional probabilities associated with the causal relationships represented by the Bayesian net. These conditional probabilities are deduced using the data sets mined from the WWW in conjunction with the data available on hand. Using heart disease data sets available at the UC Irvine Machine Learning Repository, this procedure is tested and some preliminary results are presented.