start-ver=1.4
cd-journal=joma
no-vol=1
cd-vols=
no-issue=
article-no=
start-page=690
end-page=695
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=1996
dt-pub=199610
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Pattern classification by stochastic neural network with missing data
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=<p>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</p>

en-copyright=
kn-copyright=

en-aut-name=TanakaMasahiro
en-aut-sei=Tanaka
en-aut-mei=Masahiro
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=KotokawaYasuaki
en-aut-sei=Kotokawa
en-aut-mei=Yasuaki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=TaninoTetsuzo
en-aut-sei=Tanino
en-aut-mei=Tetsuzo
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

affil-num=1
en-affil=
kn-affil=Okayama University

affil-num=2
en-affil=
kn-affil=Okayama University

affil-num=3
en-affil=
kn-affil=Okayama University
en-keyword=learning (artificial intelligence)
kn-keyword=learning (artificial intelligence)
en-keyword=neural nets
kn-keyword=neural nets
en-keyword=parameter estimation
kn-keyword=parameter estimation
en-keyword=pattern classification
kn-keyword=pattern classification
en-keyword=probability
kn-keyword=probability
END