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4 April 1997 Accuracy estimation for supervised learning algorithms
Charles W. Glover, Ed M. Oblow, Nageswara S. V. Rao
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Proceedings Volume 3077, Applications and Science of Artificial Neural Networks III; (1997) https://doi.org/10.1117/12.271548
Event: AeroSense '97, 1997, Orlando, FL, United States
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.
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Charles W. Glover, Ed M. Oblow, and Nageswara S. V. Rao "Accuracy estimation for supervised learning algorithms", Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); https://doi.org/10.1117/12.271548
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KEYWORDS
Machine learning
Error analysis
Analytical research
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