Noisy Inference and Oracles

Stephan, Frank

doi:10.1007/3-540-60454-5_38

Noisy Inference and Oracles

Frank Stephan

ALT 1995 pp. 185-200

doi:10.1007/3-540-60454-5_38 /alt/1995/stephan1995alt-noisy/

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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Text

Stephan. "Noisy Inference and Oracles." International Conference on Algorithmic Learning Theory, 1995. doi:10.1007/3-540-60454-5_38

Markdown

[Stephan. "Noisy Inference and Oracles." International Conference on Algorithmic Learning Theory, 1995.](https://mlanthology.org/alt/1995/stephan1995alt-noisy/) doi:10.1007/3-540-60454-5_38

BibTeX

@inproceedings{stephan1995alt-noisy,
  title     = {{Noisy Inference and Oracles}},
  author    = {Stephan, Frank},
  booktitle = {International Conference on Algorithmic Learning Theory},
  year      = {1995},
  pages     = {185-200},
  doi       = {10.1007/3-540-60454-5_38},
  url       = {https://mlanthology.org/alt/1995/stephan1995alt-noisy/}
}