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Abstract: this paper is to push this interaction further in light of these recent developments. In particular, we perform experiments suggested by the formal results for Adaboost and C4:5 within the weak learning framework. We concentrate on two particularly intriguing issues. First, the theoretical boosting results for top-down decision tree algorithms such as C4:5 [12] suggest that a new splitting criterion may result in trees that are smaller and more accurate than those obtained using the usual... (Update)
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BibTeX entry: (Update)
Tom Dietterich, Michael Kearns, and Yishay Mansour. Applying the weak learning framework to understand and improve C4.5. In Machine Learning: Proceedings of the Thirteenth International Conference, 1996. http://citeseer.ist.psu.edu/article/dietterich96applying.html More
@inproceedings{ dietterich96applying,
author = "Tom Dietterich and Michael Kearns and Yishay Mansour",
title = "Applying the weak learning framework to understand and improve {C}4.5",
booktitle = "Proc. 13th International Conference on Machine Learning",
publisher = "Morgan Kaufmann",
pages = "96--104",
year = "1996",
url = "citeseer.ist.psu.edu/article/dietterich96applying.html" }
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32 the boosting ability of topdown decision tree learning algor.. - Kearns, Mansour - 1996
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