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Proceedings Paper

Accuracy estimation for supervised learning algorithms
Author(s): Charles W. Glover; Ed M. Oblow; Nageswara S. V. Rao
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Paper 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.

Paper Details

Date Published: 4 April 1997
PDF: 9 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271548
Show Author Affiliations
Charles W. Glover, Oak Ridge National Lab. (United States)
Ed M. Oblow, Oak Ridge National Lab. (United States)
Nageswara S. V. Rao, Oak Ridge National Lab. (United States)


Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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