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Falsification and Future Performance

  • David Balduzzi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7070)

Abstract

We information-theoretically reformulate two measures of capacity from statistical learning theory: empirical VC-entropy and empirical Rademacher complexity. We show these capacity measures count the number of hypotheses about a dataset that a learning algorithm falsifies when it finds the classifier in its repertoire minimizing empirical risk. It then follows from that the future performance of predictors on unseen data is controlled in part by how many hypotheses the learner falsifies. As a corollary we show that empirical VC-entropy quantifies the message length of the true hypothesis in the optimal code of a particular probability distribution, the so-called actual repertoire.

Keywords

Unlabeled Data Future Performance Optimal Code Empirical Risk Hypothesis Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • David Balduzzi
    • 1
  1. 1.MPI for Intelligent SystemsTuebingenGermany

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