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An Approach to Unsupervised Learning Classification
October 1975 (vol. 24 no. 10)
pp. 979-983
R. Mizoguchi, Faculty of Engineering Science, Osaka university
In this correspondence, an approach to unsupervised pattern classifiers is discussed. The classifiers discussed here have the ability of obtaining the consistent estimates of unknown statistics of input patterns without knowing the a priori probability of each category's occurrence where the input patterns are of a mixture distribution. An analysis is made about their asymptotic behavior in order to show that the classifiers converge to the Bayes' minmum error classifier. Also, some results of a computer simulation on learning processes are shown.
Index Terms:
Bayes' classification, consistent estimates, mixture distribution, pattern recognition, two category problem, unsupervised learning.
Citation:
R. Mizoguchi, M. Shimura, "An Approach to Unsupervised Learning Classification," IEEE Transactions on Computers, vol. 24, no. 10, pp. 979-983, Oct. 1975, doi:10.1109/T-C.1975.224104
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