Weighted Order Statistic Classifiers with Large Rank-Order Margin

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.

• Type: Conference
• Year: 2003
• Where: ICML
• Authors: Reid B. Porter, Damian Eads, Don R. Hush, James Theiler
• Keywords: ICML 2003, Machine Learning, Stack Filter Function, Standard Linear Program, Weighted Order Statistics