Seminar on Computational Learning and Adaptation
Learning Comprehensible Predictive Models from Data
Mike Pazzani
Department of Information and Computer Science
University of California
Irvine, CA
Knowledge discovery in databases is a field whose goal is to turn data
into information. For example, by analyzing a database of credit card
customers we can determine what types of customers are most likely to
be profitable for the company. By "mining" databases of medical
records, new cost-effective procedures for screening for diseases may
be uncovered. Several decades of research in statistics, neural
networks and artificial intelligence have identified a variety of
approaches that produce accurate descriptive or predictive
models. However, experts are unwilling to accept the results of these
techniques when they don't make sense. Here, we focus on producing
models of data that do not unnecessarily violate the existing
knowledge of a domain, and show that the results of such a system are
more understandable by human experts.
Date: Thurs., January 29; Time: 4:15-5:30PM; Place: Gates 100
The goal of this seminar is to increase
communication among local researchers with interests in computational
approaches to learning and adaptation. If you would like to be added
to (or removed from) the mailing list, or if you are interested in
giving a talk in the seminar, please send email to
iba@isle.org.
Return to seminar schedule.