## 抄録

When there are multiple component predictors, it is promising to integrate them into one predictor for advanced reasoning. If each component predictor is given as a stochastic model in the form of probability distribution, an exponential mixture of the component probability distributions provides a good way to integrate them. However, weight parameters used in the exponential mixture model are difficult to estimate if there is no data for performance evaluation. As a suboptimal way to solve this problem, weight parameters may be estimated so that the exponential mixture model should be a balance point that is defined as an equilibrium point with respect to the distance from/to all component probability distributions. In this paper, we propose a weight parameter estimation method that represents this concept using a symmetric Kullback-Leibler divergence and discuss the features of this method.

本文言語 | English |
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ホスト出版物のタイトル | 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 |

出版社 | Institute of Electrical and Electronics Engineers Inc. |

ページ | 1126-1129 |

ページ数 | 4 |

ISBN（電子版） | 9781479959556 |

DOI | |

出版ステータス | Published - 2014 2 18 |

外部発表 | はい |

イベント | 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 - Kitakyushu, Japan 継続期間: 2014 12 3 → 2014 12 6 |

### Other

Other | 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 |
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Country | Japan |

City | Kitakyushu |

Period | 14/12/3 → 14/12/6 |

## ASJC Scopus subject areas

- Software
- Artificial Intelligence