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Seminar Details


Date 15-9-2005
Time 11:30
Room/Location Sala conferenze- num. 322 - 3 piano
Title Advances in Statistical Learning from Near-neighbors
Speaker Maya Gupta, Asst. Professor, EE, University of Washington
Affiliation University of Washington, College of Engineering
Link http://ee.washington.edu/research/guptalab/
Abstract We consider the problem of classifying or labeling a test sample based on a database of known samples and their labels. This is a standard statistical learning problem often solved by neural nets, support vector machines, Gaussian mixture models, or decision trees. In this talk research is presented into methods which learn based on near-neighbors; simple examples of this local learning approach are k-nearest neighbor and linear interpolation. Theoretically, we discuss how estimation bias can be reduced by using a convex neighborhood of samples, and by using weights that solve a linear interpolation and maximum entropy objective (LIME). Given weighted neighbors, we show that Bayesian minimum expected risk estimates will significantly outperform maximum likelihood estimates for classification when costs are asymmetric, as is often the case in medical, defense, and non-destructive evaluation applications. Applications include protein structure prediction, the non-destructive evaluation of pipeline integrity, and the automatic creation of custom color enhancements. Short Bio: Maya Gupta joined the University of Washington as an Assistant Professor of Electrical Engineering in 2003. An NSF Graduate Fellow, she completed the M.S. in EE in 1999 and the Ph.D. in 2003 at Stanford University, working with Bob Gray and Richard Olshen. From 1999-2003 she worked for Ricoh's California Research Center as a color image processing research engineer, and is the primary author on a number of patents in color image processing. She took her BS in Electrical Engineering, and a BA in Economics from Rice University, 1997. Gupta has also worked for AT&T Labs, NATO's SACLANT Undersea Research Center, Hewlett Packard, and Microsoft. More information about her research is available at her group's
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