Abstract
In document community support vector machines and naïve bayes classifier are known for their simplistic yet excellent performance. Normally the feature subsets used by these two approaches complement each other, however a little has been done to combine them. The essence of this paper is a linear classifier, very similar to these two. We propose a novel way of combining these two approaches, which synthesizes best of them into a hybrid model. We evaluate the proposed approach using 20ng dataset, and compare it with its counterparts. The efficacy of our results strongly corroborate the effectiveness of our approach.
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© 2012 Springer-Verlag Berlin Heidelberg
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Nandanwar, S., Murty, M.N. (2012). A Regularized Linear Classifier for Effective Text Classification. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_27
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DOI: https://doi.org/10.1007/978-3-642-34481-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34480-0
Online ISBN: 978-3-642-34481-7
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