Applications of penalized likelihood methods for feature selection in statistical modeling
Feature selection plays a pivotal role in knowledge discovery and contemporary scientific research. Traditional best subset selection or stepwise regression can be computationally expensive or unstable in the selection process, and so various penalized likelihood methods (PLMs) have received much a...
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| Language: | English |
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University of British Columbia
2012
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| Online Access: | http://hdl.handle.net/2429/43254 |