Authors: Stojković, Ivan
Jelisavčić, Vladisav 
Milutinović, Veljko
Obradović, Zoran
Title: Distance based modeling of interactions in structured regression
Journal: IJCAI International Joint Conference on Artificial Intelligence
Volume: 2016-January
First page: 2032
Last page: 2038
Conference: 25th International Joint Conference on Artificial Intelligence, IJCAI 2016; New York; United States; 9 July 2016 through 15 July 2016
Issue Date: 1-Jan-2016
Rank: M31
ISSN: 1045-0823
Graphical models, as applied to multi-target prediction problems, commonly utilize interaction terms to impose structure among the output variables. Often, such structure is based on the assumption that related outputs need to be similar and interaction terms that force them to be closer are adopted. Here we relax that assumption and propose a feature that is based on distance and can adapt to ensure that variables have smaller or larger difference in values. We utilized a Gaussian Conditional Random Field model, where we have extended its originally proposed interaction potential to include a distance term. The extended model is compared to the baseline in various structured regression setups. An increase in predictive accuracy was observed on both synthetic examples and real-world applications, including challenging tasks from climate and healthcare domains.
Publisher: International Joint Conferences on Artificial Intelligence
Project: DARPA, Grant FA9550-12-1-0406
NSF BIGDATA, Grant 14476570
ONR, Grant N00014-15-1-2729

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