Machine learning despite unknown classes

Authors:
Christopher B. Smith
Affiliations:
Southwest Research Institute, San Antonio, Tx
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

This paper revisits supervised machine learning for multiclass problems with the assumption that all classes cannot be represented in a training set. This is common in many applications in which there are numerous classes or in which some classes are exceedingly rare. In this paper we propose the use of a decision function to serve in place of the decision boundaries which are used in many machine learning techniques. We demonstrate this technique using Fisher's iris data and an application to language recognition.