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Abstract: In this paper, we describe an automated learning approach to text categorization based on perceptron learning and a new feature selection metric, called correlation coefficient. Our approach has been tested on the standard Reuters text categorization collection. Empirical results indicate that our approach outperforms the best published results on this Reuters collection. In particular, our new feature selection method yields considerable improvement. We also investigate the usability of our... (Update)
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BibTeX entry: (Update)
T.H.Ng, W.B.Goh, and K.L. Low, "Feature selection, perceptron learning and a usability case study for text categorization", 20 th ACM SIGIR Conference, 1997. http://citeseer.ist.psu.edu/ng97feature.html More
@inproceedings{ ng97feature,
author = "Hwee T. Ng and Wei B. Goh and Kok L. Low",
title = "Feature selection, perceptron learning, and a usability case study for text categorization",
booktitle = "Proceedings of {SIGIR}-97, 20th {ACM} International Conference on Research and Development in Information Retrieval",
publisher = "ACM Press, New York, US",
address = "Philadelphia, US",
editor = "Nicholas J. Belkin and A. Desai Narasimhalu and Peter Willett",
pages = "67--73",
year = "1997",
url = "citeseer.ist.psu.edu/ng97feature.html" }
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