@article {72,
	title = {A survey on independence-based Markov networks learning},
	journal = {Artificial Intelligence Review},
	volume = {42},
	year = {2012},
	month = {06/2012},
	pages = {1093},
	chapter = {1069},
	abstract = {<p>The problem of learning\&nbsp;the Markov network structure\&nbsp;from data\&nbsp;has become increasingly important in machine learning,<br />
and in many other application fields.\&nbsp;Markov networks are probabilistic graphical models,\&nbsp;a widely used formalism for handling probability distributions in intelligent systems.\&nbsp;This document focuses on a technology called \emph{independence-based} learning,\&nbsp;which allows for the learning of the independence structure of Markov networks from data in an efficient and sound manner,\&nbsp;whenever the dataset is sufficiently large, and data is a representative sample of the target distribution.\&nbsp;In the analysis of such technology, this work surveys the current state-of-the-art algorithms,\&nbsp;discussing its limitations, and posing a series of open problems\&nbsp;where future work may produce some advances in the area, in terms of quality and efficiency.</p>
},
	keywords = {independence-based, Markov networks, Structure learning, survey},
	issn = {1573-7462},
	doi = {10.1007/s10462-012-9346-y},
	url = {http://www.springerlink.com/content/e0l3113827341422},
	author = {Federico Schl{\"u}ter}
}
