01450nas a2200157 4500008004100000245007700041210006900118260005400187520092200241653002901163100001601192700001301208700001801221700001301239856004001252 2010 eng d00aA Rate Distortion Approach for Semi-Supervised Conditional Random Fields0 aRate Distortion Approach for SemiSupervised Conditional Random F bAdvances in Neural Information Processing Systems3 aWe propose a novel information theoretic approach for semi-supervised learning of conditional random fields that defines a training objective to combine the conditional likelihood on labeled data and the mutual information on unlabeled data. In contrast to previous minimum conditional entropy semi-supervised discriminative learning methods, our approach is grounded on a more solid foundation, the rate distortion theory in information theory. We analyze the tractability of the framework for structured prediction and present a convergent variational training algorithm to defy the combinatorial explosion of terms in the sum over label configurations. Our experimental results show the rate distortion approach outperforms standard l2 regularization, minimum conditional entropy regularization as well as maximum conditional entropy regularization on both multi-class classifcation and sequence labeling problems.10asemi-supervised learning1 aHaffari, G.1 aWang, Y.1 aWang, Shaojun1 aMori, G. uhttp://knoesis.wright.edu/node/105800370nas a2200133 4500008004100000245004100041210004100082100001800123700001600141700001300157700001300170700001300183856004000196 2008 eng d00aBoosting with Incomplete Information0 aBoosting with Incomplete Information1 aWang, Shaojun1 aHaffari, G.1 aJiao, F.1 aWang, Y.1 aMori, G. uhttp://knoesis.wright.edu/node/1125