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Noisy inference and oracles

  • Frank Stephan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 997)

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.

Keywords

Recursive Function Infinite Sequence Inductive Inference Correct Item Faulty Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Frank Stephan
    • 1
  1. 1.Institut für Logik, Komplexität und DeduktionssystemeUniversität KarlsruheKarlsruheGermany

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