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IS&T Seminar: Error Estimation and Classification in the Context of Prior Knowledge

July 10, 2013
Time: 3:00 - 4:00 PM
Location: TA-3, Bldg. 1690, Room 102 (CNLS Conference Room)

Speaker: Edward R. Dougherty, Texas A&M University

Abstract:  Epistemologically, the most important aspect of a classifier is its error rate because this rate characterizes its predictive capacity. Since, absent knowledge of the feature-label distribution, the error rate must be estimated, error estimation is critical to classification. Absent any prior knowledge whatsoever, that is, in a completely distribution-free scenario, accurate error estimation in small-sample design is highly problematic. In particular, the training data must be used for error estimation. Very rarely are any performance bounds known and, when they are known, they require very large samples, so they are not applicable to small-sample settings. The alternative to distribution-free methods is to utilize prior knowledge in the form of a prior distribution on an uncertainty class of feature-label distributions. In this setting, given sufficient prior knowledge, good error estimation can be achieved. Moreover, since it is necessary to utilize prior knowledge for error estimation, one may as well forego classification rules altogether and simply find the optimal MSE classifier, given the prior knowledge and the data. This talk discusses the difficulties of small-sample error estimation, error estimation in the context of prior knowledge, and MSE-optimal classifier design.

Biography:  Edward R. Dougherty is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University in College Station, TX, where he holds the Robert M. Kennedy ‘26 Chair in Electrical Engineering and is Director of the Genomic Signal Processing Laboratory. He is also co-Director of the Computational Biology Division of the Translational Genomics Research Institute in Phoenix, AZ. He holds a Ph.D. in mathematics from Rutgers University and an M.S. in Computer Science from Stevens Institute of Technology, and has been awarded the Doctor Honoris Causa by the Tampere University of Technology in Finland. He is a fellow of both IEEE and SPIE, has received the SPIE President’s Award, and served as the editor of the SPIE/IS&T Journal of Electronic Imaging. At Texas A&M University he has received the Association of Former Students Distinguished Achievement Award in Research, been named Fellow of the Texas Engineering Experiment Station, named Halliburton Professor of the Dwight Look College of Engineering, and recently awarded the title of Distinguished Professor. Prof. Dougherty is author of 16 books, editor of 5 others, and author of 300 journal papers.

For more information contact the technical host Frank Alexander, fja@lanl.gov, 665-4518.

Download announcement here.

Hosted by the Information Science and Technology Institute (ISTI)

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