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

研究成果: Conference contribution

1 被引用数 (Scopus)

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
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 32014 12 6

Other

Other2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
CountryJapan
CityKitakyushu
Period14/12/314/12/6

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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