An EM approach to grammatical inference: input/output HMMs
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[1] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[2] Carl H. Smith,et al. Inductive Inference: Theory and Methods , 1983, CSUR.
[3] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[4] C. Lee Giles,et al. Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks , 1992, Neural Computation.
[5] Raymond L. Watrous,et al. Induction of Finite-State Languages Using Second-Order Recurrent Networks , 1992, Neural Computation.
[6] Yoshua Bengio,et al. Credit Assignment through Time: Alternatives to Backpropagation , 1993, NIPS.
[7] Michael C. Mozer,et al. A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction , 1993, NIPS.
[8] Robert A. Jacobs,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.
[9] 大西 仁,et al. Pearl, J. (1988, second printing 1991). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan-Kaufmann. , 1994 .
[10] P. Frasconi,et al. An EM Approach to Learning Sequential , 1994 .
[11] Yoshua Bengio,et al. Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.