Advanced Statistical Learning Theory

Posted in Science on September 19, 2008

Advanced Statistical Learning Theory

This set of lectures will complement the statistical learning theory course and focus on recent advances in the domain of classification.

  1. PAC Bayesian bounds: a simple derivation, comparison with Rademacher averages.
  2. Local Rademacher complexity with classification loss, Talagrand's inequality. Tsybakov noise conditions.
  3. Properties of loss functions for classification (influence on approximation and estimation, relationship with noise conditions).
  4. Applications to SVM - Estimation and approximation properties, role of eigenvalues of the Gram matrix.

Author: Olivier Bousquet, Google

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