<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The estimation of the generalization error of a trained classifier by means of a test set is one of the oldest problems in pattern recognition and machine learning. Despite this problem has been addressed for several decades, it seems that the last word has not been written yet, because 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, so to understand if the new proposals represent an effective improvement on old ones.</p></abstract>

The estimation of the generalization error of a trained classifier by means of a test set is one of the oldest problems in pattern recognition and machine learning. Despite this problem has been addressed for several decades, it seems that the last word has not been written yet, because 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, so to understand if the new proposals represent an effective improvement on old ones.

A Survey of old and New Results for the Test Error Estimation of a Classifier

Department of Biophysical and Electronic Engineering, University of Genova, Via Opera Pia 11A, 16145 Genova, Italy

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A Survey of old and New Results for the Test Error Estimation of a Classifier"}

The estimation of the generalization error of a trained classifier by means of a test set is one of the oldest problems in pattern recognition and machine...

A Survey of old and New Results for the Test Error Estimation of a Classifier

Online veröffentlicht: 30. Dez. 2014

Seitenbereich: 229 - 242

DOI: https://doi.org/10.2478/jaiscr-2014-0016

© 2015

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.