Paper Info

Title | ||
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Unsupervised weight parameter estimation for exponential mixture distribution based on symmetric Kullback-Leibler divergence |

Abstract | ||
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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. |

Year | DOI | Venue |
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2014 | 10.1109/SCIS-ISIS.2014.7044722 | IEICE Transactions |

Keywords | DocType | Volume |

exponential distribution,inference mechanisms,parameter estimation,advanced reasoning,component probability distributions,exponential mixture,exponential mixture distribution,performance evaluation,probability distribution,stochastic model,symmetric kullback-leibler divergence,unsupervised weight parameter estimation,ensemble learning,exponential mixture model | Conference | 98-A |

Issue | ISSN | Citations |

11 | 2377-6870 | 0 |

PageRank | References | Authors |

0.34 | 5 | 1 |

Authors (1 rows)

Cited by (0 rows)

References (5 rows)

Name | Order | Citations | PageRank |
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Masato Uchida | 1 | 150 | 20.79 |