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Mathematics Based on Learning

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Algorithmic Learning Theory (ALT 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2533))

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Abstract

Learning theoretic aspects ofmathematics and logic have been studied by many authors. They study how mathematical and logical objects are algorithmically “learned” (inferred) from finite data. Although the subjects ofstudies are mathematical objects, the objective ofthe studies are learning. In this paper, a mathematics ofwhic h foundation itselfis learning theoretic will be introduced. It is called Limit-Computable Mathematics. It was originally introduced as a means for “ProofAnimation,” which is expected to make interactive formal proof development easier. Although the original objective was not learning theoretic at all, learning theory is indispensable for our research.

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Author information

Authors and Affiliations

  1. Kobe University, Rokko-dai, 657-8501, Nada, Kobe, Japan

    Susumu Hayashi

Authors
  1. Susumu Hayashi
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Editor information

Editors and Affiliations

  1. Dipartimento di Tecnologie dell’Informazione, Università degli Studi di Milano, via Bramante 65, 26013, Crema (CR), Italy

    Nicolò Cesa-Bianchi

  2. Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ohokayama Meguro Ward, 152-8552, Tokyo, Japan

    Masayuki Numao

  3. Institut für Theoretische Informatik, Universität zu Lübeck, Wallstr. 40, 23560, Lübeck, Germany

    Rüdiger Reischuk

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© 2002 Springer-Verlag Berlin Heidelberg

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Hayashi, S. (2002). Mathematics Based on Learning. In: Cesa-Bianchi, N., Numao, M., Reischuk, R. (eds) Algorithmic Learning Theory. ALT 2002. Lecture Notes in Computer Science(), vol 2533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36169-3_2

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  • DOI: https://doi.org/10.1007/3-540-36169-3_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00170-6

  • Online ISBN: 978-3-540-36169-5

  • eBook Packages: Springer Book Archive

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