GRAND seminar Flexible Metrics in Machine Learning Abstract While pattern classification has shown promise in many areas of practical significance, it faces difficult challenges posed by real world problems, of which the most pronounced is Bellman's curse of dimensionality. As a consequence, similarity functions that use all input features with equal relevance may not be effective in high dimensional spaces. Severe bias can be introduced in a high dimensional feature space with finite samples. In this talk we discuss recent approaches developed to estimate feature relevance, and to define distance functions accordingly for classification, clustering, and semi-supervised clustering. Experimental results from a variety of application domains will be presented, including microarray data analysis and text classification. Biography Carlotta Domeniconi received the Laurea Degree in computer science from the University of Milano, Milan, Italy, in 1992, the M.S. degree in information and communication technologies from the International Institute for Advanced Scientific Studies, Salerno, Italy, in 1997, and the Ph.D. degree in computer science from the University of California, Riverside, in 2002. She is currently an Assistant Professor in the Information and Software Engineering Department at George Mason University. Her research interests include machine learning, pattern recognition, data mining, and feature relevance estimation, with applications in text mining and bioinformatics. Her research is in part supported by an NSF CAREER Award and a grant from the U.S. Army.
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