Learning behaviors of automata from multiplicity and equivalence queries
CIAC '94 Proceedings of the second Italian conference on Algorithms and complexity
Learning functions represented as multiplicity automata
Journal of the ACM (JACM)
Automata: Theoretic Aspects of Formal Power Series
Automata: Theoretic Aspects of Formal Power Series
Probabilistic DFA Inference using Kullback-Leibler Divergence and Minimality
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning Stochastic Regular Grammars by Means of a State Merging Method
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
On the applications of multiplicity automata in learning
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Learning Rational Stochastic Tree Languages
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Towards Feasible PAC-Learning of Probabilistic Deterministic Finite Automata
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
On Rational Stochastic Languages
Fundamenta Informaticae
Absolute Convergence of Rational Series Is Semi-decidable
LATA '09 Proceedings of the 3rd International Conference on Language and Automata Theory and Applications
Grammatical inference as a principal component analysis problem
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Boosting Classifiers Built from Different Subsets of Features
Fundamenta Informaticae
Learning languages with rational kernels
COLT'07 Proceedings of the 20th annual conference on Learning theory
MAT learners for recognizable tree languages and tree series
Acta Cybernetica
A lower bound for learning distributions generated by probabilistic automata
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Absolute convergence of rational series is semi-decidable
Information and Computation
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
On Rational Stochastic Languages
Fundamenta Informaticae
Learning probabilistic automata: A study in state distinguishability
Theoretical Computer Science
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Given a finite set of words w1, ..., wnindependently drawn according to a fixed unknown distribution law P called a stochastic language, a usual goal in Grammatical Inference is to infer an estimate of P in some class of probabilistic models, such as Probabilistic Automata (PA). Here, we study the class ${{\mathcal S}_{\mathbb R}^{rat}(\Sigma)}$of rational stochastic languages, which consists in stochastic languages that can be generated by Multiplicity Automata (MA) and which strictly includes the class of stochastic languages generated by PA. Rational stochastic languages have minimal normal representation which may be very concise, and whose parameters can be efficiently estimated from stochastic samples. We design an efficient inference algorithm DEES which aims at building a minimal normal representation of the target. Despite the fact that no recursively enumerable class of MA computes exactly ${{\mathcal S}_{\mathbb Q}^{rat}(\Sigma)}$, we show that DEES strongly identifies ${{\mathcal S}_{\mathbb Q}^{rat}(\Sigma)}$in the limit. We study the intermediary MA output by DEES and show that they compute rational series which converge absolutely and which can be used to provide stochastic languages which closely estimate the target.