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International Conference on Algorithmic Learning Theory

ALT 2015: Algorithmic Learning Theory pp 379–394Cite as

Two Problems for Sophistication

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9355))

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.

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Author information

Authors and Affiliations

  1. System and Network Engineering Group, University of Amsterdam, Amsterdam, The Netherlands

    Peter Bloem, Steven de Rooij & Pieter Adriaans

Authors
  1. Peter Bloem
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  2. Steven de Rooij
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  3. Pieter Adriaans
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Corresponding author

Correspondence to Peter Bloem .

Editor information

Editors and Affiliations

  1. University of California, La Jolla, California, USA

    Kamalika Chaudhuri

  2. UNIV DEL INSUBRIA, 21100 VARESE, Italy

    CLAUDIO GENTILE

  3. REGINA, Saskatchewan, Canada

    Sandra Zilles

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© 2015 Springer International Publishing Switzerland

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Bloem, P., de Rooij, S., Adriaans, P. (2015). Two Problems for Sophistication. In: Chaudhuri, K., GENTILE, C., Zilles, S. (eds) Algorithmic Learning Theory. ALT 2015. Lecture Notes in Computer Science(), vol 9355. Springer, Cham. https://doi.org/10.1007/978-3-319-24486-0_25

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  • DOI: https://doi.org/10.1007/978-3-319-24486-0_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24485-3

  • Online ISBN: 978-3-319-24486-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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