Improved Gaussian Mixture Density
نویسنده
چکیده
We compare two regularization methods which can be used to improve the generalization capabilities of Gaussian mixture density estimates. The rst method consists of deening a Bayesian prior distribution on the parameter space. We derive EM (Expectation Maximization) update rules which maximize the a posterior parameter probability in contrast to the usual EM rules for Gaussian mixtures which maximize the likelihood function. In the second approach we apply ensemble averaging to density estimation. This includes Breiman's "bagging", which has recently been found to produce impressive results for classiication networks. To our knowledge this is the rst time that ensemble averaging is applied to improve density estimation.
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