no code implementations • 15 Oct 2014 • Taisuke Sato, Keiichi Kubota, Yoshitaka Kameya
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.
no code implementations • 22 Mar 2013 • Taisuke Sato, Keiichi Kubota
Third since VT always deals with a single probability of a single explanation, Viterbi explanation, the exclusiveness condition that is imposed on PRISM programs is no more required if we learn parameters by VT. Last but not least we can say that as VT in PRISM is general and applicable to any PRISM program, it largely reduces the need for the user to develop a specific VT algorithm for a specific model.
