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The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences

TitleThe Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences
Publication TypeConference Paper
Year of Publication2014
AuthorsEdera A, Strappa Y, Bromberg F
Conference Name14th edition of the Ibero-American Conference on Artificial Intelligence
Date Published11/2014
PublisherLecture Notes in Computer Science/Lecture Notes in Artificial Intelligence LNCS/LNAI series
Conference LocationSantiago de Chile
ISBN Number978-3-319-12027-0
Abstract

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.

URLhttp://www.springer.com/computer/ai/book/978-3-319-12026-3
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