**Author: Frank Stephan**.

Email: fstephan@math.uni-heidelberg.de

**Source: ***Theoretical Computer Science* Vol. 185,
No. 1, 1997, 129-157.

**Abstract.**
The present paper deals with several variants of inductive inference from
noisy data. The notion of noise is based on the idea that the learner
recieves a sequence of data elements such that each correct element
appears infinitely often and each incorrect element appears at most
finitely often. The main result is that the concept of learning in the limit
from noisy informant has the same power as finite learning using a
*K*-oracle from noise-free informant. The analog equality for text fails in
general and holds only in one direction in the case of learning uniformly
recursive families. Furthermore, learnability from noisy informant or
text in presence of using oracles is investigated. It is shown that partial
identification of all r.e. sets can also cope with noisy informant and text.

©Copyright 1997 Elsevier Science