@conference {88,
	title = {The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences},
	booktitle = {14th edition of the Ibero-American Conference on Artificial Intelligence},
	year = {2014},
	month = {11/2014},
	publisher = {Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence LNCS/LNAI series},
	organization = {Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence LNCS/LNAI series},
	address = {Santiago de Chile},
	abstract = {<p>Markov networks are models for compactly representing complex probability distributions. They are composed by a structure and a set of numerical weights. The structure qualitatively describes independences in the distribution, which can be exploited to factorize the distribution into a set of compact functions. A key application for learning structures from data is to automatically discover knowledge. In practice, structure learning algorithms focused on "knowledge discovery" present a limitation: they use a coarse-grained representation of the structure. As a result, this representation cannot describe context-specific independences. Very recently, an algorithm called CSPC was designed to overcome this limitation, but it has a high computational complexity. This work tries to mitigate this downside presenting CSGS, an algorithm that uses the Grow-Shrink strategy for reducing unnecessary computations. On an empirical evaluation, the structures learned by CSGS achieve competitive accuracies and lower computational complexity with respect to those obtained by CSPC.</p>
},
	isbn = {978-3-319-12027-0},
	url = {http://www.springer.com/computer/ai/book/978-3-319-12026-3},
	author = {Edera, Alejandro and Strappa, Yanela and Bromberg, Facundo}
}
