Elements of information theory
Elements of information theory
On the Computational Complexity of Approximating Distributions by Probabilistic Automata
Machine Learning - Computational learning theory
Efficient learning of typical finite automata from random walks
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Learning and robust learning of product distributions
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
On the learnability of discrete distributions
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
A HIDDEN MARKOV MODEL THAT FINDS GENES IN E. COLI DNA
A HIDDEN MARKOV MODEL THAT FINDS GENES IN E. COLI DNA
On the learnability and usage of acyclic probabilistic finite automata
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Predicting nearly as well as the best pruning of a decision tree
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Predicting Nearly As Well As the Best Pruning of a Decision Tree
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
Using and combining predictors that specialize
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Learning Markov chains with variable memory length from noisy output
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Dynamic Discretization of Continuous Values from Time Series
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Shallow Parsing Using Probabilistic Grammatical Inference
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Mistake-driven mixture of hierarchical tag context trees
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Part-of-speech tagging using a Variable Memory Markov model
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Extended models and tools for high-performance part-of-speech tagger
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Probabilistic Finite-State Machines-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimodal estimation of user interruptibility for smart mobile telephones
Proceedings of the 8th international conference on Multimodal interfaces
Journal of Artificial Intelligence Research
A bibliographical study of grammatical inference
Pattern Recognition
Exploring group moving pattern for an energy-constrained object tracking sensor network
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Learning finite state machines
FSMNLP'09 Proceedings of the 8th international conference on Finite-state methods and natural language processing
Task allocation learning in a multiagent environment: Application to the RoboCupRescue simulation
Multiagent and Grid Systems
An intelligent memory model for short-term prediction: an application to global solar radiation data
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Binding statistical and machine learning models for short-term forecasting of global solar radiation
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Context-aware document recommendation by mining sequential access data
Proceedings of the 1st International Workshop on Context Discovery and Data Mining
Hi-index | 0.00 |
We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic finite automata which we name Probabilistic Finite Suffix Automata. The learning algorithm is motivated by real applications in man-machine interaction such as hand-writing and speech recognition. Conventionally used fixed memory Markov and hidden Markov models have either severe practical or theoretical drawbacks. Though general hardness results are known for learning distributions generated by sources with similar structure, we prove that our algorithm can indeed efficiently learn distributions generated by our more restricted sources. In Particular, we show that the KL-divergence between the distribution generated by the target source and the distribution generated by our hypothesis can be made small with high confidence in polynomial time and sample complexity. We demonstrate the applicability of our algorithm by learning the structure of natural English text and using our hypothesis for the correction of corrupted text.