Accuracy estimation for supervised learning algorithms

Charles W. Glover, Ed M. Oblow, Nageswara S. Rao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper illustrates and discusses the relative merits of three methods - k-fold Cross Validation, Error Bounds, and Incremental Halting Test - to estimate the accuracy of a supervised learning algorithm. For each of the three methods we point out the problem they address, some of the important assumptions that they are based on, and illustrate them through an example. Finally, we discuss the relative advantages and disadvantages of each method.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsSteven K. Rogers
PublisherSociety of Photo-Optical Instrumentation Engineers
Pages794-802
Number of pages9
ISBN (Print)0819424927
StatePublished - 1997
EventApplications and Science of Artificial Neural Networks III - Orlando, FL, USA
Duration: Apr 21 1997Apr 24 1997

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume3077
ISSN (Print)0277-786X

Conference

ConferenceApplications and Science of Artificial Neural Networks III
CityOrlando, FL, USA
Period04/21/9704/24/97

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