Confidence estimation using the incremental learning algorithm, learn

Authors:
Jeffrey Byorick;Robi Polikar
Affiliations:
Electrical and Computer Engineering, Rowan University, Glassboro, NJ;Electrical and Computer Engineering, Rowan University, Glassboro, NJ
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

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Abstract

Pattern recognition problems span a broad range of applications, where each application has its own tolerance on classification error. The varying levels of risk associated with many pattern recognition applications indicate the need for an algorithm with the ability to measure its own confidence. In this work, the supervised incremental learning algorithm Learn++ [1], which exploits the synergistic power of an ensemble of classifiers, is further developed to add the capability of assessing its own confidence using a weighted exponential majority voting technique.