Pattern classification by stochastic neural network with missing data

ID	30056
フルテキストURL	fulltext.pdf 429 KB
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
巻	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	10.1109/ICSMC.1996.569878
Submission Path	industrial_engineering/54