Weighted order statistic classifiers with large rank-order margin. Metadata
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- Main Title Weighted order statistic classifiers with large rank-order margin.
Author: Porter, R. B. (Reid B.)Creator Type: Personal
Author: Hush, D. R. (Donald R.)Creator Type: Personal
Author: Theiler, J. P. (James P.)Creator Type: Personal
Author: Gokhale, M. (Maya)Creator Type: Personal
Sponsor: United States. Department of Energy.Contributor Type: Organization
Name: Los Alamos National LaboratoryPlace of Publication: United States
- Creation: 2003-01-01
- Content Description: We describe how Stack Filters and Weighted Order Statistic function classes can be used for classification problems. This leads to a new design criteria for linear classifiers when inputs are binary-valued and weights are positive . We present a rank-based measure of margin that can be directly optimized as a standard linear program and investigate its effect on generalization error with experiment. Our approach can robustly combine large numbers of base hypothesis and easily implement known priors through regularization.
- Physical Description:  p.
- Keyword: Statistics
- Keyword: Classification
- Keyword: Computers
- Keyword: Hypothesis
- STI Subject Categories: 97 Mathematical Methods And Computing
- Keyword: Learning
- Keyword: Design
- Conference: Submitted to: 20th International Conference on Machine Learning, August 2003, Washington
Name: Office of Scientific & Technical Information Technical ReportsCode: OSTI
Name: UNT Libraries Government Documents DepartmentCode: UNTGD
- Report No.: LA-UR-03-0545
- Grant Number: none
- Office of Scientific & Technical Information Report Number: 976524
- Archival Resource Key: ark:/67531/metadc928443