Actively Learning Probabilistic Subsequential Transducers
Proceedings of the Eleventh International Conference on Grammatical Inference, PMLR 21:19-33, 2012.
In this paper we investigate learning of probabilistic subsequential transducers in an active learning environment. In our learning algorithm the learner interacts with an oracle by asking probabilistic queries on the observed data. We prove our algorithm in an identification in the limit model. We also provide experimental evidence to show the correctness and to analyze the learnability of the proposed algorithm.