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Maximum Entropy Principle in Non-ordered Setting

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Algorithmic Learning Theory (ALT 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3244))

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Abstract

We consider the Maximum Entropy principle for non-ordered data in a non-probabilistic setting. The main goal of this paper is to deduce asymptotic relations for the frequencies of the energy levels in a non-ordered sequence ω N=[ω 1,...,ω N ] from the assumption of maximality of the Kolmogorov complexity K(ω N) given a constraint \(\sum\limits_{i=1}^N f(\omega_i)=N E\), where E is a number and f is a numerical function.

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Author information

Authors and Affiliations

  1. Moscow State University, Vorob’evy gory, Moscow, 119992, Russia

    Victor Maslov

  2. Institute for Information Transmission Problems, Russian Academy of Sciences, Bol’shoi Karetnyi per. 19, Moscow, GSP-4, 127994, Russia

    Vladimir V’yugin

Authors
  1. Victor Maslov
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  2. Vladimir V’yugin
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Editor information

Editors and Affiliations

  1. David R. Cheriton School of Computer Science University of Waterloo,  

    Shoham Ben-David

  2. Department of Computer & Information Sciences, University of Delaware, 103 Smith Hall, DE 19716, Newark

    John Case

  3. Dept. of Information Technology and Electronics, Ishinomaki Senshu University,  

    Akira Maruoka

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Maslov, V., V’yugin, V. (2004). Maximum Entropy Principle in Non-ordered Setting. In: Ben-David, S., Case, J., Maruoka, A. (eds) Algorithmic Learning Theory. ALT 2004. Lecture Notes in Computer Science(), vol 3244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30215-5_18

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  • DOI: https://doi.org/10.1007/978-3-540-30215-5_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23356-5

  • Online ISBN: 978-3-540-30215-5

  • eBook Packages: Springer Book Archive

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