|
About
NeuroCOLT
Papers
Archive
Books
info@neurocolt.org
|
NeuroCOLT
Technical Report NC-TR-99-046
Cross-validation
for binary classification by real-valued functions: theoretical analysis
Martin Anthony & Sean Holden
Abstract
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 analyse theoretically the accuracy
of the holdout and cross-validation estimators for the case where
real-valued functions are used as classifiers. We then introduce two
new error estimation techniques, which we call the adaptive holdout
estimate and the adaptive cross-validation estimate, and we perform
a similar analysis for these. Finally, we show how our results can
be applied to certain types of neural net-work.
Download Compressed Postscript
|