Matched-pair classification [electronic resource]
Saved in:
| Online Access: |
Online Access |
|---|---|
| Corporate Author: | |
| Format: | Government Document Electronic eBook |
| Language: | English |
| Published: |
Washington, D.C. : Oak Ridge, Tenn. :
United States. Dept. of Energy ; distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy,
2009.
|
| Subjects: |
| Abstract: | Following an analogous distinction in statistical hypothesis testing, we investigate variants of machine learning where the training set comes in matched pairs. We demonstrate that even conventional classifiers can exhibit improved performance when the input data has a matched-pair structure. Online algorithms, in particular, converge quicker when the data is presented in pairs. In some scenarios (such as the weak signal detection problem), matched pairs can be generated from independent samples, with the effect not only doubling the nominal size of the training set, but of providing the structure that leads to better learning. A family of 'dipole' algorithms is introduced that explicitly takes advantage of matched-pair structure in the input data and leads to further performance gains. Finally, we illustrate the application of matched-pair learning to chemical plume detection in hyperspectral imagery. |
|---|---|
| Item Description: | Published through the Information Bridge: DOE Scientific and Technical Information. 01/01/2009. "la-ur-09-03588" " la-ur-09-3588" Neural Information Processing Systems ; July 10, 2009 ; Vancouver, BC Canada. Theiler, James P. |