# Structure Learning Using Forced Pruning

## TL;DR

This paper introduces a computationally efficient greedy heuristic for learning Markov network structures that limits the number of parameters and performs comparably to state-of-the-art methods on real datasets.

## Contribution

It proposes a novel greedy heuristic for structure learning in Markov networks that controls model complexity and is computationally tractable.

## Key findings

- Performs comparably to state-of-the-art methods on real datasets
- Results show effective control of model complexity
- Method is computationally efficient

## Abstract

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.