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| Seminar Schedule (Seminar Organizer: Prof. Ziv Bar-Joseph) | |
Machine Learning Lunch Seminar
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Spring 2008 Seminar
Date: April 7, 2008 Time: 4:30 PM - 5:30 PM (Refreshments at 4:15) Location: 7500 Wean Hall Speaker: Jerome Friedman Professor, Stanford University Title: Fast Sparse Regression and Classification Abstract: Regularized regression and classification methods fit a linear model to data, based on some loss criterion, subject to a constraint on the coefficient values. As special cases, ridge-regression, the lasso, and subset selection all use squared-error loss with different particular constraint choices. For large problems the general choice of loss/constraint combinations is usually limited by the computation required to obtain the corresponding solution estimates, especially when non convex constraints are used to induce very sparse solutions. A fast algorithm is presented that produces solutions that closely approximate those for any convex loss and a wide variety of convex and non convex constraints, permitting application to very large problems. The benefits of this generality are illustrated by examples.
