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Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields

Gould, Stephen

Description

The standard approach to max-margin parameter learning for Markov random fields (MRFs) involves incrementally adding the most violated constraints during each iteration of the algorithm. This requires exact MAP inference, which is intractable for many classes of MRF. In this paper, we propose an exact MAP inference algorithm for binary MRFs containing a class of higher-order models, known as lower linear envelope potentials. Our algorithm is polynomial in the number of variables and number of...[Show more]

CollectionsANU Research Publications
Date published: 2011
Type: Conference paper
URI: http://hdl.handle.net/1885/39564
Source: Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields

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