Weighted Order Statistic Classifiers with Large Rank-Order Margin

Reid Porter, Damian Eads, Don Hush, and James Theiler

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 measure 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.

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