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 ∞ norm is used. We also investigate queries that are sufficient for the class to be learnable.
This work was supported in part by the IST Programme of the European Community, under the Pascal Network of Excellence, IST-2002-506778. This publication only reflects the authors’ views.
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de la Higuera, C., Oncina, J. (2004). Learning Stochastic Finite Automata. In: Paliouras, G., Sakakibara, Y. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2004. Lecture Notes in Computer Science(), vol 3264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30195-0_16
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DOI: https://doi.org/10.1007/978-3-540-30195-0_16
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