{"id":156370,"date":"2008-12-01T00:00:00","date_gmt":"2008-12-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/the-maximum-entropy-model-with-continuous-features\/"},"modified":"2018-10-16T20:22:33","modified_gmt":"2018-10-17T03:22:33","slug":"the-maximum-entropy-model-with-continuous-features","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/the-maximum-entropy-model-with-continuous-features\/","title":{"rendered":"The Maximum Entropy Model with Continuous Features"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We present the maximum entropy (MaxEnt) model with continuous features. We show that for the continuous features the weights should be continuous functions instead of single values. We propose a spline interpolation based solution to the optimization problem that contains continuous weights and illustrate that the optimization problem can be converted into a standard log-linear one without continuous weights at a higher-dimensional space.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present the maximum entropy (MaxEnt) model with continuous features. We show that for the continuous features the weights should be continuous functions instead of single values. We propose a spline interpolation based solution to the optimization problem that contains continuous weights and illustrate that the optimization problem can be converted into a standard log-linear 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