Title :
Validation of nearest neighbor classifiers
Author_Institution :
DEpt. of Math. & Comput. Sci., Richmond Univ., VA, USA
fDate :
11/1/2000 12:00:00 AM
Abstract :
This article presents a method to bound the out-of-sample error rate of a nearest neighbor classifier. The bound is based only on the examples that comprise the classifier. Thus all available examples can be used in the classifier; no examples need to be withheld to compute error bounds. The estimate used in the bound is an extension of the holdout estimate. The difference in error rates between the holdout classifier and the classifier consisting of all available examples is estimated using truncated inclusion and exclusion
Keywords :
error statistics; image classification; learning (artificial intelligence); error bounds; holdout classifier; holdout estimate; machine learning; nearest neighbor classifiers; out-of-sample error rate bound; satellite images; truncated exclusion; truncated inclusion; Equations; Error analysis; Machine learning; Nearest neighbor searches; Probability; Quality control; Sequential analysis; Statistics; Stochastic processes; Upper bound;
Journal_Title :
Information Theory, IEEE Transactions on
