Learning of Indexed Language Families from Stochastic Input
Department of Computer Science,
James Cook University,
Townsville Q 4811, Australia.
Dipartimento di Elettronica ed Informatica,
Universita di Padova,
Via Gradenigo 6/A,
35131 Padova, Italy.
Language learning from positive data in the Gold model of
inductive inference is investigated in a setting where the data can be
modeled as a stochastic process. Specifically, the input strings are
assumed to be generated from a sequence of identically distributed,
independent random variables, where the distribution depends on the
language being presented. It is shown that the indexed families of
languages learnable in a distribution-free fashion with probability
greater than 1/2 are exactly those that can be learned from all texts.
Conditions are established under which a technique can be employed to boost
the probability of success at learning.
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