Synthesizing Learners Tolerating Computable Noisy Data
Authors: John Case and Sanjay Jain
Source: Lecture Notes in Artificial Intelligence Vol. 1501,
1998, 205 - 219.
An index for an r.e. class of languages (by definition)
generates a sequence of grammars defining the class.
An index for an indexed family of languages (by definition)
generates a sequence of decision procedures
defining the family.
F. Stephan's model of noisy data is employed, in which, roughly, correct data
crops up infinitely often, and incorrect data only finitely often.
In a completely computable universe, all data sequences, even noisy ones, are
computable. New to the present paper is the restriction that noisy data
sequences be, nonetheless, computable!
Studied, then, is the synthesis from indices for r.e. classes and for
indexed families of languages
of various kinds of noise-tolerant language-learners for the
corresponding classes or families indexed, where the noisy input data
sequences are restricted to being computable.
Many positive results, as well as some negative results, are presented
regarding the existence of such synthesizers.
The main positive result is surprisingly more positive than its analog
in the case the noisy data is not restricted to being computable:
each indexed family can be learned behaviorally correctly from
computable, noisy, positive data!
The proof of another positive synthesis
result yields, as a pleasant corollary,
a strict subset-principle or tell-tale style
for the computable noise-tolerant
behaviorally correct learnability of grammars from positive and negative data,
of the corresponding families indexed.
©Copyright 1998 Springer