Faculty Candidate Seminar: Regularization and Variable Selection via the Elastic Net

Event Details
  • Date/Time:
    • Tuesday February 1, 2005 - Monday January 31, 2005
      10:00 am - 11:00 pm
  • Location: Room 228, Executive Classroom, ISyE Main Building
  • Phone:
  • URL:
  • Email:
  • Fee(s):
    N/A
  • Extras:
Contact
Barbara Christopher
Industrial and Systems Engineering
Contact Barbara Christopher
404.385.3102
Summaries

Summary Sentence: Faculty Candidate Seminar: Regularization and Variable Selection via the Elastic Net

Full Summary: Faculty Candidate Seminar: Regularization and Variable Selection via the Elastic Net

Regularization and Variable Selection via the Elastic Net

Hui Zou
Department of Statistics
Stanford University

In the practice of statistical modeling, it is often desirable to have an accurate predictive model with a sparse representation. The lasso is a promising model building technique, performing continuous shrinkage and variable selection simultaneously. Although the lasso has shown success in many situations, it may produce unsatisfactory results in some scenarios: (1) the number of predictors (greatly) exceeds the number of observations; (2) the predictors are highly correlated and form

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H. Milton Stewart School of Industrial and Systems Engineering (ISYE)

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Seminar/Lecture/Colloquium
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Status
  • Created By: Barbara Christopher
  • Workflow Status: Published
  • Created On: Oct 8, 2010 - 7:38am
  • Last Updated: Oct 7, 2016 - 9:52pm