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Abstract

We evaluate the performance of both of the proposed methods on several density estimation tasks from the UCI Repository as well as our corpus of Monkey vocalizations, recorded at the Oregon National Primate Research Center. We find that our methods represent the data consistently better than Gaussian mixture models with equivalent number of parameters. We also evaluate our proposed methods for building generative classifiers for a number of classification tasks from the UCI Repository. We find that these generative models perform as well or better than discriminative classifiers such as a Support Vector Machine (SVM).

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