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COLT
1992
Springer

Language Learning from Stochastic Input

13 years 7 months ago
Language Learning from Stochastic Input
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 form a sequence of identically distributed, independent random variables, where the distribution depends on the language being presented. A scheme is developed which can be tuned to learn, with probability one, any family of recursive languages, given a recursive enumeration of total indices for the languages in the family and a procedure to compute a lower bound to the probability of occurrence of a given string in a given language. Variations of the scheme work under other assumptions, e.g., if the probabilities of the strings form a monotone sequence with respect to a given enumeration. The learning algorithm is rather simple and appears psychologically plausible. A more sophisticated version of the learner is also developed, based on a probabilistic version of the notion of t...
Shyam Kapur, Gianfranco Bilardi
Added 09 Aug 2010
Updated 09 Aug 2010
Type Conference
Year 1992
Where COLT
Authors Shyam Kapur, Gianfranco Bilardi
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