Work

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

Year: 2011

Type: conference-paper

Abstract: 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 MRFscontainingaclassofhigher-ordermodels, knownaslower linear envelope potentials. Our algorithm is polynomial in the number of variables and number of linear envelope functions. With tractable inference in hand, we show how the parameters and corresponding feature vectors can be represented in a max-margin framework for efficiently learning lower linear envelope potentials.

Source: ANU Open Research (Australian National University)

Author: Stephen Jay Gould

Institution: Australian National University

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Eunice Kennedy Shriver National Institute of Child Health and Human Development

Award: Iodine Nutrition and Thyroid Disease During Pregnancy and Lactation

OpenAlex ID: W23134529

FWCI: 2.382

Cites: 23

Cited by: 12

Related to: 10