### 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 training samples 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 generalize this method.

元の言語 | English |
---|---|

ページ（範囲） | 2349-2353 |

ページ数 | 5 |

ジャーナル | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences |

巻 | E98A |

発行部数 | 11 |

DOI | |

出版物ステータス | Published - 2015 11 1 |

外部発表 | Yes |

### Fingerprint

### ASJC Scopus subject areas

- Signal Processing
- Computer Graphics and Computer-Aided Design
- Applied Mathematics
- Electrical and Electronic Engineering

### これを引用

**Unsupervised weight parameter estimation for exponential mixture distribution based on symmetric kullback-leibler divergence.** / Uchida, Masato.

研究成果: Article

*IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences*, 巻. E98A, 番号 11, pp. 2349-2353. https://doi.org/10.1587/transfun.E98.A.2349

}

TY - JOUR

T1 - Unsupervised weight parameter estimation for exponential mixture distribution based on symmetric kullback-leibler divergence

AU - Uchida, Masato

PY - 2015/11/1

Y1 - 2015/11/1

N2 - 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 training samples 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 generalize this method.

AB - 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 training samples 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 generalize this method.

KW - Ensemble learning

KW - Exponential mixture model

KW - Parameter estimation

KW - Symmetric Kullback-Leibler divergence

UR - http://www.scopus.com/inward/record.url?scp=84947998264&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84947998264&partnerID=8YFLogxK

U2 - 10.1587/transfun.E98.A.2349

DO - 10.1587/transfun.E98.A.2349

M3 - Article

AN - SCOPUS:84947998264

VL - E98A

SP - 2349

EP - 2353

JO - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

JF - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

SN - 0916-8508

IS - 11

ER -