Weighted Order Statistic Classifiers with Large Rank-Order Margin
Reid B. Porter, D. R. Hush, J. P. Theiler, M. Gokhale · University of North Texas Digital Library (University of North Texas) · 2003
We investigate how stack filter function classes like weighted order statistics can be applied to 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 mea-sure of margin that is directly optimized as a standard linear program and investigate its relationship to regularization. Our approach can robustly combine large numbers of base hypothesis and has similar performance to other types of regularization. 1.