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Effects of kolmogorov complexity present in inductive inference as well

  • University of Maryland, College Park
  • The University of Auckland
  • University of Bonn
  • Institutionen för matematik och fysik
  • University of Latvia

Research output: Chapter in Book/Report/Conference proceedingConference paperResearchpeer-review

5 Citations (Scopus)

Abstract

For all complexity measures in Kolmogoro complexity the effect discovered by P. Martin-LSf holds. For every infinite binary sequence there is a wide gap between the supremum and the infimum of the complexity of initial fragments of the sequence. It is assumed that that this inevitable gap is characteristic of Kolmogorov complexity, and it is caused by the highly abstract nature of the unrestricted Kolmogorov complexity. We consider the complexity of inductive inference for recursively enumerable classes of total recursive functions. This object is considered as a rather simple object where no effects from highly abstract theories are likely to be met. Here, similar gaps were discovered. Moreover, the existence of these gaps is proved by an explicit use of the theorem by P. Martin-Löf. In our paper, we study a modification of inductive inference complexity. The complexity is usually understood as the maximum of mindchanges over the functions defined by the first n indices of the numbering. Instead we consider the mindchange complexity as the maximum over the first n functions in the numbering (disregarding the repeated functions). Linear upper and lower bounds for the mindchange complexity are proved. ttowever, the gap between bounds for all n and bounds for infinitely many n remains.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 8th International Workshop, ALT 1997, Proceedings
EditorsMing Li, Akira Maruoka
PublisherSpringer Verlag
Pages244-259
Number of pages16
ISBN (Print)3540635777, 9783540635772
DOIs
Publication statusPublished - 1997
Event8th International Workshop on Algorithmic Learning Theory, ALT 1997 - Sendai, Japan
Duration: 6 Oct 19978 Oct 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1316
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Workshop on Algorithmic Learning Theory, ALT 1997
Country/TerritoryJapan
CitySendai
Period6/10/978/10/97

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