Abstract Details
|
Activity Number:
|
399
|
|
Type:
|
Invited
|
|
Date/Time:
|
Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
| Abstract - #307237 |
|
Title:
|
Sufficient Dimension Reduction in Binary Classification
|
|
Author(s):
|
Seung Jun Shin and Yichao Wu*+ and Hao Helen Zhang and Yufeng Liu
|
|
Companies:
|
North Carolina State University and NC State University and University of Arizona and The University of North Carolina
|
|
Keywords:
|
binary classification ;
sufficient dimension reduction
|
|
Abstract:
|
Reducing dimensionality of data is essential for binary classification with high-dimensional covariates. In the context of sufficient dimension reduction (SDR), most, if not all, existing SDR methods suffer in binary classification. In this talk, we target directly at the SDR for binary classification and propose a new method based on support vector machines. The new method is supported by both numerical evidence and theoretical justification.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 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.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.