PAC-learnability of Probabilistic Deterministic Finite State Automata
Alexander Clark and Franck Thollard
Journal of Machine Learning Research
We study the learnability of Probabilistic Deterministic Finite State Automata under a modified PAC-learning criterion. We argue that it is necessary to add additional parameters to the sample complexity polynomial, namely a bound on the expected length of strings generated from any state, and a bound on the distinguishability between states. With this, we demonstrate that the class of PDFAs is PAC-learnable using a variant of a standard state-merging algorithm and the Kullback-Leibler divergence as error function.