Statistical learning procedure in loopy belief propagation for probabilistic image processing

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

We give a fast and practical algorithm for statistical learning hyperparameters from observable data in probabilistic image processing, which is based on Gaussian graphical model and maximum likelihood estimation. Although hyperparameters in the probabilistic model are determined so as to maximize a marginal likelihood, a practical algorithm is described for the EM algorithm with the loopy belief propagation which is one of approximate inference algorithms in artificial intelligence.

本文言語English
ホスト出版物のタイトルProceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Interne
ページ741-746
ページ数6
出版ステータスPublished - 2005 12 1
イベントInternational Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IAWTIC 2005 - Vienna, Austria
継続期間: 2005 11 282005 11 30

出版物シリーズ

名前Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet
2

Other

OtherInternational Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IAWTIC 2005
国/地域Austria
CityVienna
Period05/11/2805/11/30

ASJC Scopus subject areas

  • 工学(全般)

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