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Frequency Prediction of Functions

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Mathematical and Engineering Methods in Computer Science (MEMICS 2011)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 7119))

Abstract

Prediction of functions is one of processes considered in inductive inference. There is a “black box” with a given total function f in it. The result of the inductive inference machine F( < f(0), f(1), ⋯ ,f(n) > ) is expected to be f(n + 1). Deterministic and probabilistic prediction of functions has been widely studied. Frequency computation is a mechanism used to combine features of deterministic and probabilistic algorithms. Frequency computation has been used for several types of inductive inference, especially, for learning via queries. We study frequency prediction of functions and show that that there exists an interesting hierarchy of predictable classes of functions.

The research was supported by Grant No. 09.1570 from the Latvian Council of Science and by Project 2009/0216/1DP/1.1.1.2.0/09/IPIA/VIA/044 from the European Social Fund.

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Author information

Authors and Affiliations

  1. Faculty of Computing, University of Latvia, Raiņa bulvāris 29, Riga, LV-1459, Latvia

    Kaspars Balodis, Ilja Kucevalovs & Rūsiņš Freivalds

Authors
  1. Kaspars Balodis
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  2. Ilja Kucevalovs
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  3. Rūsiņš Freivalds
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Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Brno University of Technology, Božetěchova 2, 612 66, Brno, Czech Republic

    Zdeněk Kotásek , Lukáš Sekanina  & Tomáš Vojnar ,  & 

  2. Faculty of Informatics, Masaryk University, Botanická 68a, 60200, Brno, Czech Republic

    Jan Bouda  & Ivana Černá  & 

  3. Institute of Computer Science, Masaryk University, Botanická 68a, 60200, Brno, Czech Republic

    David Antoš

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Balodis, K., Kucevalovs, I., Freivalds, R. (2012). Frequency Prediction of Functions. In: Kotásek, Z., Bouda, J., Černá, I., Sekanina, L., Vojnar, T., Antoš, D. (eds) Mathematical and Engineering Methods in Computer Science. MEMICS 2011. Lecture Notes in Computer Science, vol 7119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25929-6_7

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  • DOI: https://doi.org/10.1007/978-3-642-25929-6_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25928-9

  • Online ISBN: 978-3-642-25929-6

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