  Markov networks are widely used in many Machine Learning applications
including natural language processing, computer vision, and bioinformatics .
Learning Markov networks have many complications ranging from intractable
computations involved to the possibility of learning a model with a huge number
of parameters. In this report, we provide a computationally tractable greedy
heuristic for learning Markov networks structure. The proposed heuristic
results in a model with a limited predefined number of parameters. We ran our
method on 3 fully-observed real datasets, and we observed that our method is
doing comparably good to the state of the art methods.
