Cross-Validation for Binary Classification by Real-Valued Functions: Theoretical Analysis

This paper concerns the use of real-valued functions for binary classification problems. Previous work in this area has concentrated on using as an error estimate the `resubstitution' error (that is, the empirical error of a classifier on the training sample) or its derivatives. However, in practice, cross-validation and related techniques are more popular. Here, we devise new holdout and cross-validation estimators for the case where real-valued functions are used as classifiers, and we analyse theoretically the accuracy of these. Contents

• Type: Conference
• Year: 1998
• Where: COLT
• Authors: Martin Anthony, Sean B. Holden
• Keywords: Binary Classification Problems, COLT 1998, Error Estimate, Machine Learning, Real-valued Functions