Regularization Paths and Coordinate Descent
author: Trevor Hastie,
Stanford University
published: Sept. 26, 2008, recorded: August 2008, views: 17501
published: Sept. 26, 2008, recorded: August 2008, views: 17501
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Description
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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In a measurable world confronted with a blast of information, regularization has turned into a significant fixing. In numerous issues, we have a lot a greater number of factors than perceptions, and the tether punishment and its half and halves have become progressively valuable.
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