Showing papers on "Unsupervised learning published in 1975"
TL;DR: In this correspondence, an approach to unsupervised pattern classifiers is discussed and an analysis is made about their asymptotic behavior to show that the classifiers converge to the Bayes' minmum error classifier.
Abstract: 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.
8 citations
TL;DR: Test implementation of this scheme using the remotely sensed agricultural data of the Purdue laboratory for agricultural remote sensing in a simulated unsupervised mode, has brought out the efficacy of this integrated system of feature selection and learning.
Abstract: Here the twin problems of feature selection and learning are tackled simultaneously to obtain a unified approach to the problem of pattern recognition in an unsupervised environment. This is achieved by combining a feature selection scheme based on the stochastic learning automata model with an unsupervised learning scheme such as learning with a probabilistic teacher. Test implementation of this scheme using the remotely sensed agricultural data of the Purdue laboratory for agricultural remote sensing (LARS) in a simulated unsupervised mode, has brought out the efficacy of this integrated system of feature selection and learning.
5 citations