This work provide a short study of training algorithms useful for adaptation of recurrent connectionist models for symbolic time series modeling tasks. We show that approaches based on Kalman filtration outperform standard gradinet based training algorithms. We propose simple approximation to the Kalman filtration with favorable computational requirements and on several linguistic time series taken from recently published papers we demonstrate superior ability of the proposed method. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Čerňanský, M., & Beňušková, Ľ. (2009). Training recurrent connectionist models on symbolic time series. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5506 LNCS, pp. 285–292). https://doi.org/10.1007/978-3-642-02490-0_35
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