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Graphical model structure learning using L₁-regularization

University of British Columbia

This work looks at fitting probabilistic graphical models to data when the structure is not known. The main tool to do this is L₁-regularization and the more general group L₁-regularization. We describe limited-memory quasi-Newton methods to solve optimization problems with these types of regularizers, and we examine learning directed acyclic graphical models with L₁-regularization, learning undirected graphical models with group L₁-regularization, and learning hierarchical log-linear models with overlapping group L₁-regularization.

Text

2010-08-11

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/27277

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/