This contribution presents a new class of MRF, that is inspired by methods of statistical physics. The new energy function assumes full-connectivity in the neighborhood system and...
We examine the theoretical and numerical global convergence properties of a certain "gradient free" stochastic approximation algorithm called the "simultaneous pertu...
To classify a large number of unlabeled examples we combine a limited number of labeled examples with a Markov random walk representation over the unlabeled examples. The random w...
This paper provides a performance evaluation of the TETRAPOL random access protocol. The results are based on a Markovian model which is also presented. The Markovian model is use...
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hid...
John D. Lafferty, Andrew McCallum, Fernando C. N. ...