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In recent years, deep discriminative models have achieved extraordinary performance on supervised learning tasks, significantly outperforming their generative counterparts. However, their success relies on the presence of a large amount of labeled data. How can one use the same discriminative models for learning useful features in the absence of labels? We address this question in this paper, by jointly...
Maximum entropy approach to classification is very well studied in applied statistics and machine learning and almost all the methods that exists in literature are discriminative in nature. In this paper, we introduce a maximum entropy classification method with feature selection for large dimensional data such as text datasets that is generative in nature. To tackle the curse of dimensionality of...
In maximum entropy method, one chooses a distribution from a set of distributions that maximizes the Shannon entropy for making inference from incomplete information. There are various ways to specify this set of distributions, the important special case being when this set is described by mean-value constraints of some feature functions. In this case, maximum entropy method fixes an exponential distribution...
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