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Tanaka, Masahiro Okayama University
Kotokawa, Yasuaki Okayama University
Tanino, Tetsuzo Okayama University
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
learning (artificial intelligence)
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.
Copyright © 1996 IEEE. All rights reserved.