### Abstract

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.

Original language | English |
---|---|

Pages (from-to) | 2349-2353 |

Number of pages | 5 |

Journal | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences |

Volume | E98A |

Issue number | 11 |

DOIs | |

Publication status | Published - 2015 Nov 1 |

Externally published | Yes |

### Fingerprint

### Keywords

- Ensemble learning
- Exponential mixture model
- Parameter estimation
- Symmetric Kullback-Leibler divergence

### ASJC Scopus subject areas

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

### Cite this

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

Research output: Contribution to journal › Article

*IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences*, vol. E98A, no. 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

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 -