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Noisy inference and oracles

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Algorithmic Learning Theory (ALT 1995)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 997))

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Abstract

A learner noisily infers a function or set, if every correct item is presented infinitely often while in addition some incorrect data (”noise”) is presented a finite number of times. It is shown that learning from a noisy informant is equal to finite learning with K-oracle from a usual informant. This result has several variants for learning from text and using different oracles. Furthermore, partial identification of all r.e. sets can cope also with noisy input.

Supported by the Deutsche Forschungsgemeinschaft (DFG) grant Me 672/4-2.

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Authors and Affiliations

  1. Institut für Logik, Komplexität und Deduktionssysteme, Universität Karlsruhe, 76128, Karlsruhe, Germany

    Frank Stephan

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  1. Frank Stephan
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Klaus P. Jantke Takeshi Shinohara Thomas Zeugmann

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© 1995 Springer-Verlag Berlin Heidelberg

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Stephan, F. (1995). Noisy inference and oracles. In: Jantke, K.P., Shinohara, T., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 1995. Lecture Notes in Computer Science, vol 997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60454-5_38

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  • DOI: https://doi.org/10.1007/3-540-60454-5_38

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60454-9

  • Online ISBN: 978-3-540-47470-8

  • eBook Packages: Springer Book Archive

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