Improved Gaussian Mixture Density Estimates Using Bayesian Penalty Terms and Network Averaging
نویسندگان
چکیده
Volker Tresp Siemens AG Central Research 81730 Munchen, Germany Volker. [email protected] We compare two regularization methods which can be used to improve the generalization capabilities of Gaussian mixture density estimates. The first method uses a Bayesian prior on the parameter space. We derive EM (Expectation Maximization) update rules which maximize the a posterior parameter probability. In the second approach we apply ensemble averaging to density estimation. This includes Breiman's "bagging" , which recently has been found to produce impressive results for classification networks.
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تاریخ انتشار 1995