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Abstract: Real-world applications of text categorization often require a system to deal with tens of thousands of categories de- ned over a large taxonomy. This paper addresses the problem with respect to a set of popular algorithms in text categorization, including Support Vector Machines, k-nearest neighbor, ridge regression, linear least square t and logistic regression. By providing a formal analysis of the computational complexity of each classi cation method, followed by an investigation on the... (Update)
Cited by: More
Text Categorization - Sebastiani (2005) (Correct)
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BibTeX entry: (Update)
Yang, Y., A scalability analysis of classifiers in text categorization. Proceedings of SIGIR-03, 26th ACM International Conference on Research and Development in Information Retrieval,ACM Press, New York, US: Toronto, CA, 2003. http://citeseer.ist.psu.edu/article/yang03scalability.html More
@misc{ yang03scalability,
author = "Y. Yang",
title = "A scalability analysis of classifiers in text categorization",
text = "Yang, Y., A scalability analysis of classifiers in text categorization.
Proceedings of SIGIR-03, 26th ACM International Conference on Research and
Development in Information Retrieval,ACM Press, New York, US: Toronto, CA,
2003.",
year = "2003",
url = "citeseer.ist.psu.edu/article/yang03scalability.html" }
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