Two problems for sophistication

Authors
Publication date 2015
Host editors
  • K. Chaudhuri
  • C. Gentile
  • S. Zilles
Book title Algorithmic Learning Theory
Book subtitle 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015 : proceedings
ISBN
  • 9783319244853
ISBN (electronic)
  • 9783319244860
Series Lecture Notes in Computer Science
Event 26th International Conference on Algorithmic Learning Theory, ALT 2015
Pages (from-to) 379-394
Number of pages 16
Publisher Cham: Springer
Organisations
  • Interfacultary Research - Institute for Logic, Language and Computation (ILLC)
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
  • Faculty of Science (FNWI)
Abstract

Kolmogorov complexity measures the amount of information in data, but does not distinguish structure from noise. Kolmogorov’s definition of the structure function was the first attempt to measure only the structural information in data, by measuring the complexity of the smallest model that allows for optimal compression of the data. Since then, many variations of this idea have been proposed, for which we use sophistication as an umbrella term. We describe two fundamental problems with existing proposals, showing many of them to be unsound. Consequently, we put forward the view that the problem is fundamental: it may be impossible to objectively quantify the sophistication.

Document type Conference contribution
Language English
Published at https://doi.org/10.1007/978-3-319-24486-0_25
Other links https://www.scopus.com/pages/publications/84945943708
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