The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Abstract Details
Activity Number:
|
106
|
Type:
|
Invited
|
Date/Time:
|
Monday, August 1, 2011 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Physical and Engineering Sciences
|
Abstract - #300470 |
Title:
|
Experimental Designs for Statistical Learning
|
Author(s):
|
Xinwei Deng*+ and Peter Z. G. Qian
|
Companies:
|
University of Wisconsin at Madison
|
Address:
|
Department of Statistics, Madison, WI, 53706, USA
|
Keywords:
|
design of experiments ;
machine learning ;
cross-validation
|
Abstract:
|
This talk is devoted to showcasing the importance of design of experiments for machine learning. The basic message is if you can design better, then you can learn better. Specific topics include sliced cross-validation for estimating the error rate of a classifier, designs for the lasso and sliced designs for efficient tuning parameter selection. Joint work with Xinwei Deng at U of Wisconsin-Madison and C. Devon Lin at Queen's University, Canada.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2011 program
|
2011 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.