Communications of the ACM
Elements of information theory
Elements of information theory
On the Computational Complexity of Approximating Distributions by Probabilistic Automata
Machine Learning - Computational learning theory
Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
On the learnability of discrete distributions
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
On the learnability and usage of acyclic probabilistic finite automata
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
On the learnability and usage of acyclic probabilistic finite automata
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Learning Regular Languages from Simple Positive Examples
Machine Learning
Learning DFA from Simple Examples
Machine Learning
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Identification in the Limit with Probability One of Stochastic Deterministic Finite Automata
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Stochastic Grammatical Inference with Multinomial Tests
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Learning Probabilistic Residual Finite State Automata
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
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
Identification of DFA: data-dependent vs data-independent algorithms
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Finite-state transducers in language and speech processing
Computational Linguistics
Probabilistic Finite-State Machines-Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Partially distribution-free learning of regular languages from positive samples
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
PAC-learnability of probabilistic deterministic finite state automata in terms of variation distance
Theoretical Computer Science
Towards Feasible PAC-Learning of Probabilistic Deterministic Finite Automata
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Polynomial Time Probabilistic Learning of a Subclass of Linear Languages with Queries
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Efficient Pruning of Probabilistic Automata
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Learning PDFA with asynchronous transitions
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
A lower bound for learning distributions generated by probabilistic automata
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Covariance in Unsupervised Learning of Probabilistic Grammars
The Journal of Machine Learning Research
Formal and empirical grammatical inference
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts of ACL 2011
Research on Language and Computation
PAC-Learning of markov models with hidden state
ECML'06 Proceedings of the 17th European conference on Machine Learning
PAC-learnability of probabilistic deterministic finite state automata in terms of variation distance
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Learnability of probabilistic automata via oracles
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Identification in the limit of systematic-noisy languages
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
PAC-learning unambiguous NTS languages
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Three learnable models for the description of language
LATA'10 Proceedings of the 4th international conference on Language and Automata Theory and Applications
Polynomial time learning of some multiple context-free languages with a minimally adequate teacher
FG'10/FG'11 Proceedings of the 15th and 16th international conference on Formal Grammar
On the learnability of shuffle ideals
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
Learning probabilistic automata: A study in state distinguishability
Theoretical Computer Science
Picking up the pieces: Causal states in noisy data, and how to recover them
Pattern Recognition Letters
Software model synthesis using satisfiability solvers
Empirical Software Engineering
On the learnability of shuffle ideals
The Journal of Machine Learning Research
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We study the learnability of Probabilistic Deterministic Finite State Automata under a modified PAC-learning criterion. We argue that it is necessary to add additional parameters to the sample complexity polynomial, namely a bound on the expected length of strings generated from any state, and a bound on the distinguishability between states. With this, we demonstrate that the class of PDFAs is PAC-learnable using a variant of a standard state-merging algorithm and the Kullback-Leibler divergence as error function.