Due to travel by some of us to a DARPA-related meeting. The tea talk this week has been moved to Friday. Hope you all can make it -- this talk should be of interest to everyone and particularly for the newer students that may not have had much previous exposure to statistical learning theory. The talk will be given by Ian Goodfellow.
Date/Time: Friday Nov. 4th / 14h00 Location: LISA Lab (AA3256)
Title: Statistical Learning Theory
Abstract: I will give a tutorial on statistical learning theory. First I will show how to prove some simple probabilistic guarantees on the generalization error of a classifier in the PAC learning framework using Chebyshev's inequality. Next I will discuss how to extend these bounds to more realistic settings using the concept of Vapnik-Chervonenkis dimension. Finally I will compare structural risk minimization, a model selection principle based on statistical learning theory, to other methods of obtaining low generalization error.
Cheers, Aaron
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