Predicting Protein Secondary Structure Using Stochastic Tree Grammars
Machine Learning - Special issue on learning with probabilistic representations
Probabilistic Languages: A Review and Some Open Questions
ACM Computing Surveys (CSUR)
Stochastic Inference of Regular Tree Languages
Machine Learning
Probabilistic k-Testable Tree Languages
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Journal of Automata, Languages and Combinatorics - Special issue: Selected papers of the workshop weighted automata: Theory and applications (Dresden University of Technology (Germany), March 4-8, 2002)
Learning deterministically recognizable tree series
Journal of Automata, Languages and Combinatorics
Learning rational stochastic languages
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Using pseudo-stochastic rational languages in probabilistic grammatical inference
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Learning multiplicity tree automata
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Relevant Representations for the Inference of Rational Stochastic Tree Languages
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
A spectral approach for probabilistic grammatical inference on trees
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
On Rational Stochastic Languages
Fundamenta Informaticae
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We consider the problem of learning stochastic tree languages, i.e. probability distributions over a set of trees $T({\cal F})$, from a sample of trees independently drawn according to an unknown target P. We consider the case where the target is a rational stochastic tree language, i.e. it can be computed by a rational tree series or, equivalently, by a multiplicity tree automaton. In this paper, we provide two contributions. First, we show that rational tree series admit a canonical representation with parameters that can be efficiently estimated from samples. Then, we give an inference algorithm that identifies the class of rational stochastic tree languages in the limit with probability one.