Elements of information theory
Elements of information theory
On the Computational Complexity of Approximating Distributions by Probabilistic Automata
Machine Learning - Computational learning theory
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
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Evolutionary Trees Can be Learned in Polynomial Time in the Two-State General Markov Model
SIAM Journal on Computing
Learning Stochastic Finite Automata from Experts
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
PAC-learnability of Probabilistic Deterministic Finite State Automata
The Journal of Machine Learning Research
Some Discriminant-Based PAC Algorithms
The Journal of Machine Learning Research
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
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
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
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
On the learnability of shuffle ideals
The Journal of Machine Learning Research
| Hi-index | 5.23 |
We consider the problem of PAC-learning distributions over strings, represented by probabilistic deterministic finite automata (PDFAs). PDFAs are a probabilistic model for the generation of strings of symbols, that have been used in the context of speech and handwriting recognition, and bioinformatics. Recent work on learning PDFAs from random examples has used the KL-divergence as the error measure; here we use the variation distance. We build on recent work by Clark and Thollard, and show that the use of the variation distance allows simplifications to be made to the algorithms, and also a strengthening of the results; in particular that using the variation distance, we obtain polynomial sample size bounds that are independent of the expected length of strings.