Abstract
This paper addresses the problem of text classification in high dimensionality spaces by applying linear weight updating classifiers that have been highly studied in the domain of machine learning. Our experimental results are based on the Winnow family of algorithms that are simple to implement and efficient in terms of computation time and storage requirements. We applied an exponential multiplication function to weight updates and we experimentally calculated the optimal values of the learning rate and the separating surface parameters. Our results are at the level of the best results that were reported on the family of linear algorithms and perform nearly as well as the top performing methodologies in the literature.
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References
Aas, K., Eikvil, L.: Text categorization: A survey. Technical report, Norwegian Computing Center (1999)
Apte, C., Damerau, F., Weiss, S.: Toward language independent automated learning of text categorization models. In: Proceedings SIGIR 1994 (1994)
Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 273–297 (1995)
Dagan, I., Karov, K., Roth, D.: Mistake-Driven Learning in Text Categorization. In: Proceedings of the Second Conference on Empirical Methods in NLP, pp. 55–63 (1997)
Kivinen, J., Warmuth, M., Auer, P.: The Perceptron Algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant. Artificial Intelligence 97(1-2), 325–343 (1997)
Littlestone, N.: Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning 2, 285–318 (1988)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)
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© 2006 Springer-Verlag Berlin Heidelberg
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Gkiokas, A., Demiros, I., Piperidis, S. (2006). An Analysis of Linear Weight Updating Algorithms for Text Classification. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds) Advances in Artificial Intelligence. SETN 2006. Lecture Notes in Computer Science(), vol 3955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752912_56
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DOI: https://doi.org/10.1007/11752912_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34117-8
Online ISBN: 978-3-540-34118-5
eBook Packages: Computer ScienceComputer Science (R0)