Weighted order statistic classifiers with large rank-order margin.

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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.

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[9] p.

Creation Information

Porter, R. B. (Reid B.); Hush, D. R. (Donald R.); Theiler, J. P. (James P.) & Gokhale, M. (Maya) January 1, 2003.

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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

[9] p.

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  • Submitted to: 20th International Conference on Machine Learning, August 2003, Washington

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  • Report No.: LA-UR-03-0545
  • Grant Number: none
  • Office of Scientific & Technical Information Report Number: 976524
  • Archival Resource Key: ark:/67531/metadc928443

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • January 1, 2003

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

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  • Dec. 12, 2016, 6:17 p.m.

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Porter, R. B. (Reid B.); Hush, D. R. (Donald R.); Theiler, J. P. (James P.) & Gokhale, M. (Maya). Weighted order statistic classifiers with large rank-order margin., article, January 1, 2003; United States. (digital.library.unt.edu/ark:/67531/metadc928443/: accessed October 20, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.