## 抄録

The expectation and maximization algorithm (EM algorithm) is generalized so that the learning proceeds according to adjustable weights in terms of probability measures. The presented method, the weighted EM algorithm, or the α-EM algorithm includes the existing EM algorithm as a special case. It is further found that this learning structure can work systolically. It is also possible to add monitors to interact with lower systolic subsystems. This is made possible by attaching building blocks of the weighted (or plain) EM learning. Derivation of the whole algorithm is based on generalized divergences. In addition to the discussions on the learning, extensions of basic statistical properties such as Fisher's efficient score, his measure of information and Cramer-Rao's inequality are given. These appear in update equations of the generalized expectation learning. Experiments show that the presented generalized version contains cases that outperform traditional learning methods.

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

Place of Publication | Piscataway, NJ, United States |

出版社 | IEEE |

ページ | 1936-1941 |

ページ数 | 6 |

巻 | 3 |

出版ステータス | Published - 1997 |

イベント | Proceedings of the 1997 IEEE International Conference on Neural Networks. Part 4 (of 4) - Houston, TX, USA 継続期間: 1997 6月 9 → 1997 6月 12 |

### Other

Other | Proceedings of the 1997 IEEE International Conference on Neural Networks. Part 4 (of 4) |
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City | Houston, TX, USA |

Period | 97/6/9 → 97/6/12 |

## ASJC Scopus subject areas

- ソフトウェア
- 制御およびシステム工学
- 人工知能