Learning regular sets from queries and counterexamples
Information and Computation
The inference of tree languages from finite samples: an algebraic approach
Theoretical Computer Science
Learning functions represented as multiplicity automata
Journal of the ACM (JACM)
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)
Interactive learning of node selecting tree transducer
Machine Learning
Information extraction from web documents based on local unranked tree automaton inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning rational stochastic languages
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Learning Rational Stochastic Tree Languages
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Minimizing Deterministic Weighted Tree Automata
Language and Automata Theory and Applications
Minimizing deterministic weighted tree automata
Information and Computation
Definable transductions and weighted logics for texts
Theoretical Computer Science
MAT learners for recognizable tree languages and tree series
Acta Cybernetica
Learning deterministically recognizable tree series: revisited
CAI'07 Proceedings of the 2nd international conference on Algebraic informatics
A randomised inference algorithm for regular tree languages
Natural Language Engineering
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In this paper, we present a theoretical approach for the problem of learning multiplicity tree automata. These automata allows one to define functions which compute a number for each tree. They can be seen as a strict generalization of stochastic tree automata since they allow to define functions over any field K. A multiplicity automaton admits a support which is a non deterministic automaton. From a grammatical inference point of view, this paper presents a contribution which is original due to the combination of two important aspects. This is the first time, as far as we now, that a learning method focuses on non deterministic tree automata which computes functions over a field. The algorithm proposed in this paper stands in Angluin's exact model where a learner is allowed to use membership and equivalence queries. We show that this algorithm is polynomial in time in function of the size of the representation.