Abstract
Stochastic deterministic finite automata have been introduced and are used in a variety of settings. We report here a number of results concerning the learnability of these finite state machines. In the setting of identification in the limit with probability one, we prove that stochastic deterministic finite automata cannot be identified from only a polynomial quantity of data. If concerned with approximation results, they become PAC-learnable if the L infin; norm is used. We also investigate queries that are sufficient for the class to be learnable. © Springer-Verlag Berlin Heidelberg 2004.
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CITATION STYLE
De La Higuera, C., & Oncina, J. (2004). Learning stochastic finite automata. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3264, pp. 175–186). Springer Verlag. https://doi.org/10.1007/978-3-540-30195-0_16
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