IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of information theory
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Computational Linguistics
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Communications of the ACM
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Communications of the ACM
Handbook of Formal Languages
On the Estimation of 'Small' Probabilities by Leaving-One-Out
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic k-testable Tree Languages and Applications
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
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Computational Linguistics - Special issue on using large corpora: II
Solution of an Open Problem on Probabilistic Grammars
IEEE Transactions on Computers
Stochastic k-testable Tree Languages and Applications
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
LARS: A learning algorithm for rewriting systems
Machine Learning
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Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Learning context-free grammar using improved tabular representation
Applied Soft Computing
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IEEE Transactions on Fuzzy Systems
A bibliographical study of grammatical inference
Pattern Recognition
Smoothing and compression with stochastic k-testable tree languages
Pattern Recognition
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Acta Cybernetica
Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
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In this paper, we describe a generalization for tree stochastic languages of the k-gram models. These models are based on the k- testable class, a subclass of the languages recognizable by ascending tree automata. One of the advantages of this approach is that the probabilistic model can be updated in an incremental fashion. Another feature is that backing-off schemes can be defined. As an illustration of their applicability, they have been used to compress tree data files at a better rate than string-based methods.