BibTeX
@TECHREPORT{Wolpert93onoverfitting,
author = {David H. Wolpert},
title = {On Overfitting Avoidance As Bias},
institution = {SFI TR},
year = {1993}
}
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Abstract
In supervised learning it is commonly believed that penalizing complex functions helps one avoid "overfitting" functions to data, and therefore improves generalization. It is also commonly believed that cross-validation is an effective way to choose amongst algorithms for fitting functions to data. In a recent paper, Schaffer (1993) presents experimental evidence disputing these claims. The current paper consists of a formal analysis of these contentions of Schaffer's. It proves that his contentions are valid, although some of his experiments must be interpreted with caution.
