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Abstract: Previous research has shown that a technique called error-correcting output coding (ECOC) can dramatically improve the classification accuracy of supervised learning algorithms that learn to classify data points into one of k AE 2 classes. This paper presents an investigation of why the ECOC technique works, particularly when employed with decision-tree learning algorithms. It shows that the ECOC method--- like any form of voting or committee---can reduce the variance of the learning ... (Update)
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BibTeX entry: (Update)
Dietterich, T., & Kong, E. (1995). Error-correcting output coding corrects bias and variance. In S. Prieditis and S. Russell, eds., Proceedings of the 12th International Conference on Machine Learning. http://citeseer.ist.psu.edu/kong95errorcorrecting.html More
@inproceedings{ kong95errorcorrecting,
author = "Eun Bae Kong and Thomas G. Dietterich",
title = "Error-Correcting Output Coding Corrects Bias and Variance",
booktitle = "International Conference on Machine Learning",
pages = "313-321",
year = "1995",
url = "citeseer.ist.psu.edu/kong95errorcorrecting.html" }
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