Communications of the ACM
Dynamic programming inference of Markov networks from finite sets of sample strings
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient algorithm for the inference of circuit-free automata
Syntactic and structural pattern recognition
Some Relations Among Stochastic Finite State Networks Used in Automatic Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polynomial learnability of probabilistic concepts with respect to the Kullback-Leibler divergence
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Elements of information theory
Elements of information theory
A Learning Criterion for Stochastic Rules
Machine Learning - Computational learning theory
On the Computational Complexity of Approximating Distributions by Probabilistic Automata
Machine Learning - Computational learning theory
Fundamentals of speech recognition
Fundamentals of speech recognition
Learning probabilistic automata with variable memory length
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Efficient distribution-free learning of probabilistic concepts
Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects
On the learnability of discrete distributions
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Learning Fallible Deterministic Finite Automata
Machine Learning - Special issue on COLT '93
Approximating grammar probabilities: solution of a conjecture
Journal of the ACM (JACM)
Bayesian learning of probabilistic language models
Bayesian learning of probabilistic language models
On the learnability and usage of acyclic probabilistic finite automata
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Recent advances of grammatical inference
Theoretical Computer Science - Special issue on algorithmic learning theory
Statistical methods for speech recognition
Statistical methods for speech recognition
Stochastic Grammatical Inference of Text Database Structure
Machine Learning
Probabilistic Languages: A Review and Some Open Questions
ACM Computing Surveys (CSUR)
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Machine Learning
Using Symbol Clustering to Improve Probabilistic Automaton Inference
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
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
Smoothing Probabilistic Automata: An Error-Correcting Approach
ICGI '00 Proceedings of the 5th 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
What Is the Search Space of the Regular Inference?
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Planar Hidden Markov Modeling: From Speech to Optical Character Recognition
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
A study of grammatical inference
A study of grammatical inference
Inference of probabilistic grammars.
Inference of probabilistic grammars.
Large-vocabulary speaker-independent continuous speech recognition: the sphinx system
Large-vocabulary speaker-independent continuous speech recognition: the sphinx system
Multi-site data collection for a spoken language corpus
HLT '91 Proceedings of the workshop on Speech and Natural Language
Introduction to probabilistic automata (Computer science and applied mathematics)
Introduction to probabilistic automata (Computer science and applied mathematics)
The equivalence and learning of probabilistic automata
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
On stochastic context-free languages
Information Sciences: an International Journal
A Maximum Likelihood Approach to Continuous Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Probabilistic Finite-State Machines-Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Finite-State Machines-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Context-Free Grammars Estimated from Infinite Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
On Rational Stochastic Languages
Fundamenta Informaticae
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
Arm-hand behaviours modelling: from attention to imitation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Intent inference via syntactic tracking
Digital Signal Processing
PAC-Learning of markov models with hidden state
ECML'06 Proceedings of the 17th European conference on Machine Learning
On Rational Stochastic Languages
Fundamenta Informaticae
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
| Hi-index | 0.01 |
This article presents an overview of Probabilistic Automata (PA) and discrete Hidden Markov Models (HMMs), and aims at clarifying the links between them. The first part of this work concentrates on probability distributions generated by these models. Necessary and sufficient conditions for an automaton to define a probabilistic language are detailed. It is proved that probabilistic deterministic automata (PDFA) form a proper subclass of probabilistic non-deterministic automata (PNFA). Two families of equivalent models are described next. On one hand, HMMs and PNFA with no final probabilities generate distributions over complete finite prefix-free sets. On the other hand, HMMs with final probabilities and probabilistic automata generate distributions over strings of finite length. The second part of this article presents several learning models, which formalize the problem of PA induction or, equivalently, the problem of HMM topology induction and parameter estimation. These learning models include the PAC and identification with probability 1 frameworks. Links with Bayesian learning are also discussed. The last part of this article presents an overview of induction algorithms for PA or HMMs using state merging, state splitting, parameter pruning and error-correcting techniques.