## 抄録

Fast estimation algorithms for Hidden Markov models (HMMs) for given data are presented. These algorithms start from the alpha-EM algorithm which includes the traditional log-EM as its proper subset. Since existing or traditional HMMs are the outcome of the log-EM, it had been expected that the alpha-HMM would exist. In this paper, it is shown that this foresight is true by using methods of the iteration index shift and likelihood ratio expansion. In each iteration, new update equations utilize one-step past terms which are computed and stored during the previous maximization step. Therefore, iteration speedup directly appears as that of CPU time. Since the new method is theoretically based on the alpha-EM, all of its properties are inherited. There are eight types of alpha-HMMs derived. They are discrete, continuous, semi-continuous and discrete-continuous alpha-HMMs, and both for single and multiple sequences. Using the properties of the alpha-EM algorithm, the speedup property is theoretically analyzed. Experimental results including real world data are given.

本文言語 | English |
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ホスト出版物のタイトル | Proceedings of the International Joint Conference on Neural Networks |

ページ | 808-816 |

ページ数 | 9 |

DOI | |

出版ステータス | Published - 2011 |

イベント | 2011 International Joint Conference on Neural Network, IJCNN 2011 - San Jose, CA 継続期間: 2011 7月 31 → 2011 8月 5 |

### Other

Other | 2011 International Joint Conference on Neural Network, IJCNN 2011 |
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City | San Jose, CA |

Period | 11/7/31 → 11/8/5 |

## ASJC Scopus subject areas

- ソフトウェア
- 人工知能