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  An Analysis of the Anti-Learning Phenomenon for the Class Symmetric Polyhedron

Kowalczyk, A., & Chapelle, O. (2005). An Analysis of the Anti-Learning Phenomenon for the Class Symmetric Polyhedron. In S. Jain, H. Simon, & E. Tomita (Eds.), Algorithmic Learning Theory: 16th International Conference, ALT 2005, Singapore, October 8-11, 2005 (pp. 78-92). Berlin, Germany: Springer.

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 Creators:
Kowalczyk, A, Author
Chapelle, O1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: This paper deals with an unusual phenomenon where most machine learning algorithms yield good performance on the training set but systematically worse than random performance on the test set. This has been observed so far for some natural data sets and demonstrated for some synthetic data sets when the classification rule is learned from a small set of training samples drawn from some high dimensional space. The initial analysis presented in this paper shows that anti-learning is a property of data sets and is quite distinct from overfitting of a training data. Moreover, the analysis leads to a specification of some machine learning procedures which can overcome anti-learning and generate ma- chines able to classify training and test data consistently.

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 Dates: 2005-10
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: BibTex Citekey: 3604
DOI: 10.1007/11564089_8
 Degree: -

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Title: 16th International Conference on Algorithmic Learning Theory (ALT 2005)
Place of Event: Singapore
Start-/End Date: 2005-10-08 - 2005-10-11

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Title: Algorithmic Learning Theory: 16th International Conference, ALT 2005, Singapore, October 8-11, 2005
Source Genre: Proceedings
 Creator(s):
Jain, S, Editor
Simon, HU, Editor
Tomita, E, Editor
Affiliations:
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Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 78 - 92 Identifier: ISBN: 978-3-540-29242-5

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 3734 Sequence Number: - Start / End Page: - Identifier: -