We consider the problem of learning stochastic tree languages, i.e. probability distributions over a set of trees T(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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Denis, F., & Habrard, A. (2007). Learning rational stochastic tree languages. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4754 LNAI, pp. 242–256). Springer Verlag. https://doi.org/10.1007/978-3-540-75225-7_21
Mendeley helps you to discover research relevant for your work.