Learning Stochastic Deterministic Regular Languages
Franck Thollard and Alexander Clark
In: ICGI 2004, 11 - 13 October 2004, Athen, Greece.

Abstract

We propose in this article a new practical algorithm for inferring $\mu$-distinguishable stochastic deterministic regular languages. We prove that this algorithm will infer, with high probability, an automaton isomorphic to the target when given a polynomial number of examples. We discuss the links between the error function used to evaluate the inferred model and the learnability of the model class in a PAC like framework.

	PDF - Requires Adobe Acrobat Reader or other PDF viewer.
	Postscript - Requires a viewer, such as GhostView

[Edit]