DocumentCode
1151813
Title
On the posterior-probability estimate of the error rate of nonparametric classification rules
Author
Lugosi, Gábor ; Pawlak, Miroslaw
Author_Institution
Dept. of Math., Budapest Tech. Univ., Hungary
Volume
40
Issue
2
fYear
1994
fDate
3/1/1994 12:00:00 AM
Firstpage
475
Lastpage
481
Abstract
The posterior-probability estimate of the classification error rate of some nonparametric classification rules is studied. The variance of the estimator is shown to have same remarkable distribution-free properties for the k-nearest neighbor, kernel, and histogram rules. We also investigate the bias of the estimate and establish its consistency and upper bounds. The version of the estimate calculated from an independent set of unclassified patterns is also considered
Keywords
error statistics; estimation theory; nonparametric statistics; pattern recognition; probability; classification error rate; distribution-free properties; estimate bias; histogram rules; k-nearest neighbor rule; kernel rules; nonparametric classification rules; posterior-probability estimate; unclassified patterns; upper bounds; variance; Error analysis; Histograms; Kernel; Mathematics; Pattern recognition; Random variables; Smoothing methods; Testing; Upper bound; Yield estimation;
fLanguage
English
Journal_Title
Information Theory, IEEE Transactions on
Publisher
ieee
ISSN
0018-9448
Type
jour
DOI
10.1109/18.312167
Filename
312167
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