{"id":147882,"date":"2003-01-01T00:00:00","date_gmt":"2003-01-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/discriminative-model-selection-for-density-models\/"},"modified":"2018-10-16T20:23:15","modified_gmt":"2018-10-17T03:23:15","slug":"discriminative-model-selection-for-density-models","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/discriminative-model-selection-for-density-models\/","title":{"rendered":"Discriminative Model Selection for Density Models"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Density models are a popular tool for building classifiers. When using density models to build a classifier, one typically learns a separate density modelf or each class of interest. These density models are then combined to make a classifier through the use of Bayes\u2019 rule utilizing the prior distribution over the classes. In this paper, we provide a discriminative method for choosing among alternative density models for each class to improve classification accuracy.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Density models are a popular tool for building classifiers. When using density models to build a classifier, one typically learns a separate density modelf or each class of interest. These density models are then combined to make a classifier through the use of Bayes\u2019 rule utilizing the prior distribution over the classes. 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