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Statistical Learning Theory
This video was recorded at Machine Learning Summer School (MLSS), Tübingen 2003. This course will give a detailed introduction to learning theory with a focus on the classification problem. It will be shown how to obtain (pobabilistic) bounds on the generalization error for certain types of algorithms. The main themes will be: * probabilistic inequalities and concentration inequalities * union bounds, chaining * measuring the size of a function class, Vapnik Chervonenkis dimension, shattering dimension and Rademacher averages * classification with real-valued functions Some knowledge of probability theory would be helpful but not required since the main tools will be introduced.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
