P-values for computer-intensive classifiers
Seminar Room 1, Newton Institute
In the first part of the talk presents p-values for classification in general. The latter are an interesting alternative to classifiers or posterior distributions of class labels. Their purpose is to quantify uncertainty when classifying a single observation, even if we don't have information on the prior distribution of class labels.
After illustrating this concept with some examples and procedures, we focus on computational issues and discuss p-values involving regularization, in particular, LASSO type penalties, to cope with high-dimensional data.
(Part of this talk is based on joint work with Axel Munk, Goettingen, and Bernd-Wolfgang Igl, Luebeck.)
- http://staff.unibe.ch/duembgen/ - speaker's homepage
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