このエントリーをはてなブックマークに追加
ID 30056
フルテキストURL
著者
Tanaka, Masahiro Okayama University
Kotokawa, Yasuaki Okayama University
Tanino, Tetsuzo Okayama University
抄録

In this paper, pattern classification by stochastic neural networks is considered. This model is also called a Gaussian mixture model. When missing data exist in the training data, it is usual to remove incomplete instants. Here we take another approach, where the missing elements are estimated by using the conditional expectation based on the estimated model by using the EM algorithm. It is shown by using Fisher's Iris data that this approach is superior to removing incomplete data

キーワード
learning (artificial intelligence)
neural nets
parameter estimation
pattern classification
probability
備考
Digital Object Identifier: 10.1109/ICSMC.1996.569878
Published with permission from the copyright holder. This is the institute's copy, as published in Systems, Man, and Cybernetics, 1996., IEEE International Conference on, 14-17 Oct. 1996, Vol. 1, Pages 690-695.
Publisher URL:http://dx.doi.org/10.1109/ICSMC.1996.569878
Copyright © 1996 IEEE. All rights reserved.
発行日
1996-10
出版物タイトル
Systems
1巻
開始ページ
690
終了ページ
695
資料タイプ
学術雑誌論文
言語
English
査読
有り
DOI
Submission Path
industrial_engineering/54