Graphical Model Structure Learning with 1-Regularization
نویسنده
چکیده
This work looks at fitting probabilistic graphical models to data when the structure is not known. The main tool to do this is `1-regularization and the more general group `1-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 `1-regularization, learning undirected graphical models with group `1-regularization, and learning hierarchical loglinear models with overlapping group `1-regularization.
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