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  Falsification and future performance

Balduzzi, D. (2013). Falsification and future performance. In D. Dowe (Ed.), Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence (pp. 65-78). Berlin, Germany: Springer.

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 Creators:
Balduzzi, D1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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 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 falsies when it nds the classier 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 falsies. As a corollary we show that empirical VC-entropy quanties the message length of the true hypothesis in the optimal code of a particular probability distribution, the so-called actual repertoire.

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 Dates: 2013
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: BibTex Citekey: Balduzzi2011_3
DOI: 10.1007/978-3-642-44958-1_5
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Title: Solomonoff 85th Memorial Conference
Place of Event: Melbourne, Australia
Start-/End Date: 2011-12

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Title: Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence
Source Genre: Proceedings
 Creator(s):
Dowe, DL, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 65 - 78 Identifier: ISBN: 978-3-642-44957-4

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 7070 Sequence Number: - Start / End Page: - Identifier: -