DocumentCode
2211834
Title
Test error bounds for classifiers: A survey of old and new results
Author
Anguita, Davide ; Ghelardoni, Luca ; Ghio, Alessandro ; Ridella, Sandro
Author_Institution
DIBE, Univ. of Genova, Genova, Italy
fYear
2011
fDate
11-15 April 2011
Firstpage
80
Lastpage
87
Abstract
In this paper, we focus the attention on one of the oldest problems in pattern recognition and machine learning: the estimation of the generalization error of a classifier through a test set. Despite this problem has been addressed for several decades, the last word has not yet been written, as new proposals continue to appear in the literature. Our objective is to survey and compare old and new techniques, in terms of quality of the estimation, easiness of use, and rigorousness of the approach.
Keywords
generalisation (artificial intelligence); learning (artificial intelligence); pattern classification; classifier error; generalization error estimation; machine learning; pattern recognition; test error bounds; Chebyshev approximation; Error analysis; Estimation; Gaussian distribution; Training; Upper bound;
fLanguage
English
Publisher
ieee
Conference_Titel
Foundations of Computational Intelligence (FOCI), 2011 IEEE Symposium on
Conference_Location
Paris
Print_ISBN
978-1-4244-9981-6
Type
conf
DOI
10.1109/FOCI.2011.5949469
Filename
5949469
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