An Algorithm for Unsupervised
نویسنده
چکیده
We consider the approach to unsupervised learning whereby a normal mixture model is tted to the data by maximum likelihood. An algorithm called NMM is presented that enables the normal mixture model with either restricted or unrestricted component covariance matrices to be tted to a given data set. The algorithm automatically handles the problem of the speciication of initial values for the parameters in the iterative tting of the model within the framework of the EM algorithm. The algorithm also has the provision to carry a test for the number of components on the basis of the likelihood ratio statistic.
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تاریخ انتشار 1996