A Logic-based Approach to Generatively Defined Discriminative Modeling

15 Oct 2014 Taisuke Sato Keiichi Kubota Yoshitaka Kameya

Conditional random fields (CRFs) are usually specified by graphical models but in this paper we propose to use probabilistic logic programs and specify them generatively. Our intension is first to provide a unified approach to CRFs for complex modeling through the use of a Turing complete language and second to offer a convenient way of realizing generative-discriminative pairs in machine learning to compare generative and discriminative models and choose the best model... (read more)

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Structured Prediction