Learning regular sets from queries and counterexamples
Information and Computation
Handbook of formal languages, vol. 3
Machine Learning
Machine Learning
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Determinization of finite state weighted tree automata
Journal of Automata, Languages and Combinatorics
Simpler and more general minimization for weighted finite-state automata
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Query Learning of Regular Tree Languages: How to Avoid Dead States
Theory of Computing Systems
Learning deterministically recognizable tree series
Journal of Automata, Languages and Combinatorics
The myhill-nerode theorem for recognizable tree series
DLT'03 Proceedings of the 7th international conference on Developments in language theory
Learning a regular tree language from a teacher
DLT'03 Proceedings of the 7th international conference on Developments in language theory
Learning multiplicity tree automata
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Minimizing Deterministic Weighted Tree Automata
Language and Automata Theory and Applications
Minimizing deterministic weighted tree automata
Information and Computation
MAT learners for recognizable tree languages and tree series
Acta Cybernetica
A randomised inference algorithm for regular tree languages
Natural Language Engineering
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We generalize a learning algorithm originally devised for deterministic all-accepting weighted tree automata (wta) to the setting of arbitrary deterministic wta. The learning is exact, supervised, and uses an adapted minimal adequate teacher; a learning model introduced by Angluin. Our algorithm learns a minimal deterministic wta that recognizes the taught tree series and runs in polynomial time in the size of that wta and the size of the provided counterexamples. Compared to the original algorithm, we show how to handle non-final states in the learning process; this problem was posed as an open problem in [Drewes, Vogler: Learning Deterministically Recognizable Tree Series, J. Autom. Lang. Combin. 2007].