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Graphical model structure learning using L₁-regularization Schmidt, Mark
Abstract
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.
Item Metadata
| Title |
Graphical model structure learning using L₁-regularization
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| Creator | |
| Publisher |
University of British Columbia
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| Date Issued |
2010
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| Description |
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.
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| Genre | |
| Type | |
| Language |
eng
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| Date Available |
2010-08-11
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| Provider |
Vancouver : University of British Columbia Library
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| Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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| DOI |
10.14288/1.0051935
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| URI | |
| Degree | |
| Program | |
| Affiliation | |
| Degree Grantor |
University of British Columbia
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| Graduation Date |
2010-11
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| Campus | |
| Scholarly Level |
Graduate
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| Rights URI | |
| Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International