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Journal Article

New Approaches to Statistical Learning Theory

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Bousquet,  O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Bousquet, O. (2003). New Approaches to Statistical Learning Theory. Annals of the Institute of Statistical Mathematics, 55(2), 371-389. doi:10.1007/BF02530506.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-DD72-D
Abstract
We present new tools from probability theory that can be applied to the analysis of learning algorithms. These tools allow to derive new bounds on the generalization performance of learning algorithms and to propose alternative measures of the complexity of the learning task, which in turn can be used to derive new learning algorithms.