Improved Gaussian Mixture Density Estimates Using Bayesian Penalty Terms and Network Averaging
Dirk Ormoneit, Volker Tresp · 1995
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.