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
Learning distributions by their density levels: a paradigm for learning without a teacher
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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
Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
The consensus string problem and the complexity of comparing hidden Markov models
Journal of Computer and System Sciences - Computational biology 2002
PAC-learnability of Probabilistic Deterministic Finite State Automata
The Journal of Machine Learning Research
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
Prediction, Learning, and Games
Prediction, Learning, and Games
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
PAC-Learning of markov models with hidden state
ECML'06 Proceedings of the 17th European conference on Machine Learning
Learnability of probabilistic automata via oracles
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Learning rational stochastic languages
COLT'06 Proceedings of the 19th annual conference on Learning Theory
A spectral algorithm for learning Hidden Markov Models
Journal of Computer and System Sciences
Learning probabilistic automata: A study in state distinguishability
Theoretical Computer Science
| Hi-index | 0.00 |
Known algorithms for learning PDFA can only be shown to run in time polynomial in the so-called distinguishability µ of the target machine, besides the number of states and the usual accuracy and confidence parameters. We show that the dependence on µ is necessary for every algorithm whose structure resembles existing ones. As a technical tool, a new variant of Statistical Queries termed L∞-queries is defined. We show how these queries can be simulated from samples and observe that known PAC algorithms for learning PDFA can be rewritten to access its target using L∞-queries and standard Statistical Queries. Finally, we show a lower bound: every algorithm to learn PDFA using queries with a resonable tolerance needs a number of queries larger than (1/µ)c for every c