### Abstract

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 handwriting 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.

Original language | English (US) |
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Title of host publication | Proceedings of the 7th Annual Conference on Computational Learning Theory, COLT 1994 |

Publisher | Association for Computing Machinery |

Pages | 35-46 |

Number of pages | 12 |

ISBN (Electronic) | 0897916557 |

DOIs | |

State | Published - Jul 16 1994 |

Externally published | Yes |

Event | 7th Annual Conference on Computational Learning Theory, COLT 1994 - New Brunswick, United States Duration: Jul 12 1994 → Jul 15 1994 |

### Publication series

Name | Proceedings of the Annual ACM Conference on Computational Learning Theory |
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Volume | Part F129415 |

### Other

Other | 7th Annual Conference on Computational Learning Theory, COLT 1994 |
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Country | United States |

City | New Brunswick |

Period | 7/12/94 → 7/15/94 |

### All Science Journal Classification (ASJC) codes

- Software
- Theoretical Computer Science
- Artificial Intelligence

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## Cite this

*Proceedings of the 7th Annual Conference on Computational Learning Theory, COLT 1994*(pp. 35-46). (Proceedings of the Annual ACM Conference on Computational Learning Theory; Vol. Part F129415). Association for Computing Machinery. https://doi.org/10.1145/180139.181006