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Abstract: We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a margin-based binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class is compared against all others, or in which all pairs of classes are compared to each other, or in which output codes with error-correcting properties are used. We propose a general method for combining ... (Update)
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BibTeX entry: (Update)
Erin L. Allwein, Robert E. Schapire, and Yoram Singer. Reducing multiclass to binary: A unifying approach for margin classifiers. In Proceedings of the Seventeenth International Conference on Machine Learning, pages 9--16, 2000. http://citeseer.ist.psu.edu/article/allwein00reducing.html More
@inproceedings{ allwein00reducing,
author = "Erin L. Allwein and Robert E. Schapire and Yoram Singer",
title = "Reducing Multiclass to Binary: {A} Unifying Approach for Margin Classifiers",
booktitle = "Proc. 17th International Conf. on Machine Learning",
publisher = "Morgan Kaufmann, San Francisco, CA",
pages = "9--16",
year = "2000",
url = "citeseer.ist.psu.edu/article/allwein00reducing.html" }
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