Surrogate Cost Techniques in Classification and Regression Analysis

We study the problems of classification and regression analysis when the set of classes or parameters is a -compact metric space, by means of surrogate cost minimization. We give a natural sufficient condition for the optimal classifier or estimator to be of the form when the function minimizes a cost function which is a surrogate for the actual loss defined on pairs of classes. Sequences of functions whose expectations converge to the infimum of the expectations of all such functions can then be found by minimizing the sample averages of training sets. This result extends and sharpens previous results.

By: S. L. Hantler

Published in: RC23697 in 2005


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