This paper develops a general, formal framework for modeling term dependencies via Markov random fields. The model allows for arbitrary text features to be incorporated as eviden...
In this paper, we develop a model for representing term dependence based on Markov Random Fields and present an approach based on Markov Chain Monte Carlo technique for generating ...
We propose Markov random fields (MRFs) as a probabilistic mathematical model for unifying approaches to multi-robot coordination or, more specifically, distributed action selectio...
Jesse Butterfield, Odest Chadwicke Jenkins, Brian ...
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
Abstract--This work studies the near-optimality versus the complexity of distributed configuration management for wireless networks. We first develop a global probabilistic graphic...