Learning associative Markov networks

Markov networks are extensively used to model complex sequential, spatial, and relational interactions in fields as diverse as image processing, natural language analysis, and bioinformatics. However, inference and learning in general Markov networks is intractable. In this paper, we focus on learning a large subclass of such models (called associative Markov networks) that are tractable or closely approximable. This subclass contains networks of discrete variables with K labels each and clique potentials that favor the same labels for all variables in the clique. Such networks capture the "guilt by association" pattern of reasoning present in many domains, in which connected ("associated") variables tend to have the same label. Our approach exploits a linear programming relaxation for the task of finding the best joint assignment in such networks, which provides an approximate quadratic program (QP) for the problem of learning a marginmaximizing Markov network. We...

• Type: Conference
• Year: 2004
• Where: ICML
• Authors: Benjamin Taskar, Vassil Chatalbashev, Daphne Koller
• Keywords: Associative Markov Networks, General Markov Networks, ICML 2004, Machine Learning, Subclass Contains Networks