Classifier selection from a totally bounded class of functions

Majid Mojirsheibani

Statistics & Probability Letters, 2001, vol. 52, issue 4, 391-400

Abstract: This article deals with the two-class classification problem, where the class conditional probability [pi](x)=P{Y=1 X=x} belongs to some known class of functions . Given a data-based skeleton estimate of the class , with respect to the empirical L1-norm, we consider methods of constructing classifiers using the members of the class . Conditions under which the resulting classification rules are strongly Bayes consistent are also studied. The results are nonparametric and continue to hold regardless of the VC dimension of the corresponding class of classifiers.

Keywords: Bayes; classifier; Misclassification; error; Shatter; coefficient; Skeleton; estimate; Regularization; Consistency (search for similar items in EconPapers)
Date: 2001
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