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Statistical Learning Theory

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If you have a question about this talk, please contact Alessandro Davide Ialongo.


The wikipedia definition of Statistical Learning Theory says that it is a framework for machine learning drawing from the fields of statistics and functional analysis. It deals with the problem of finding a predictive function based on data. According to Vapnik (1990), “abstract learning theory established some conditions for generalization which are more general than those discussed in classical statistical paradigms and the understanding of these conditions inspired new algorithmic approaches to function estimation problems.” I will cover some introductory material, including the following topics:

Different loss functions Learning algorithms: Empirical risk minimisation and regularisation How can we solve each problem (first order methods only) VC dimension and some bounds

Recommended Reading

Introduction to Statistical Learning Theory, Bousquet, O., Boucheron, S. and Lugosi, G.

MIT Statistical Learning Theory and Applications course and RegML summer school notes by Lorenzo Rosasco

An Overview of Statistical Learning theory, Vapnik, V.

This talk is part of the Machine Learning Reading Group @ CUED series.

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