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.
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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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