A Theoretical Framework for Independent Classifier Combination

Abstract

The combination of classifiers from independent observation domains has a myriad of benefits in practical pattern recognition problems. In this paper we propose a firm theoretical framework from which an upper bound on classifier combination performance can be calculated, based on mismatches between train and test sets. Using this framework, insights can be… (More)

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