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Generalization to Unseen Cases: (No) Free Lunches and Good-Turing estimation

Generalization to Unseen Cases: (No) Free Lunches and Good-Turing estimation

This video was recorded at Workshop on Modelling in Classification and Statistical Learning, Eindhoven 2004. The present workshop addresses the problem of predicting a - binary - label Y from given the feature X. A procedure for classification is to be learned from a training set (X1, Y1) , ... , (Xn , Yn ). In the statistical literature on classification, the training set is traditionally seen as an i.i.d. sample from the distribution P of (X,Y), but one otherwise does not assume any a priori knowledge on P. Theoretical results have been derived that hold no matter what P is, which typically means that such results concentrate on worst cases. There are various reasons to step aside from this so-called black box approach. For example, the by now generally accepted rule ``regression is... Show More
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