Model selection of bayesian hierarchical mixture of experts based on variational inference

研究成果: Conference contribution

抄録

We consider the model selection of the hierarchical mixture of experts (HME). The HME is a tree-structured probabilistic model for regression and classification. The HME model has high prediction accuracy and high interpretability, however, the estimation of the parameters tends to overfit due to the complexity of the model. In order to mitigate the overfitting problem, in previous studies, several Bayesian estimation methods for the HME parameters have been proposed. In these studies, the true model that generates data is fixed. In general, however, the true model is unknown. Model selection is one of the most important and difficult problems of regression and classification. For the Bayesian HME, the model is determined by the tree structure, the form of the prior distribution and its parameters, however, only the tree structure is considered as a model parameter in previous studies. In this paper, we consider all of these as model parameters and extend the model selection method. Then, we propose a maximum a posteriori (MAP) estimation method of the Bayesian HME model selection. The approximate posterior probability of each model is calculated by the variational lower bound. We show the effectiveness of the proposed method by numerical experiments and discuss the results applied to actual data sets.

本文言語English
ホスト出版物のタイトル2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3474-3479
ページ数6
ISBN(電子版)9781728145693
DOI
出版ステータスPublished - 2019 10
イベント2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 - Bari, Italy
継続期間: 2019 10 62019 10 9

出版物シリーズ

名前Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
2019-October
ISSN(印刷版)1062-922X

Conference

Conference2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
国/地域Italy
CityBari
Period19/10/619/10/9

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 制御およびシステム工学
  • 人間とコンピュータの相互作用

フィンガープリント

「Model selection of bayesian hierarchical mixture of experts based on variational inference」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル