A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Approximate learning of dynamic models
Proceedings of the 1998 conference on Advances in neural information processing systems II
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Inducing Probabilistic Grammars by Bayesian Model Merging
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Parsing the Wall Street Journal with the inside-outside algorithm
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
Grammatical inference by Hill Climbing
Information Sciences: an International Journal
Graphical EM for on-line learning of grammatical probabilities in radar Electronic Support
Applied Soft Computing
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Estimating the parameters of stochastic context-free grammars (SCFGs) from data is an important, well-studied problem. Almost without exception, existing approaches make repeated passes over the training data. The memory requirements of such algorithms are ill-suited for embedded agents exposed to large amounts of training data over long periods of time. We present a novel algorithm, called HOLA, for estimating the parameters of SCFGs that computes summary statistics for each string as it is observed and then discards the string. The memory used by HOLA is bounded by the size of the grammar, not by the amount of training data. Empirical results show that HOLA performs as well as the Inside-Outside algorithm on a variety of standard problems, despite the fact that it has access to much less information.