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Regularization Paths and Coordinate Descent
This video was recorded at 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Las Vegas 2008. In a statistical world faced with an explosion of data, regularization has become an important ingredient. In many problems, we have many more variables than observations, and the lasso penalty and its hybrids have become increasingly useful. This talk presents some effective algorithms based on coordinate descent for fitting large scale regularization paths for a variety of problems. Joint work with Rob Tibshirani and Jerome Friedman
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
