Abstract
Text categorization is widely used in applications such as spam filtering, identification of document genre, authorship attribution, and automated essay grading. The rapid growth in the amount of text data gives rise to the urgent need for fast text classification algorithms. In this paper, we propose a GPU based SVM solver for large scale text datasets. Using Platt’s Sequential Minimal Optimization algorithm, we achieve a speedup of 5–40 times over LibSVM running on a high-end traditional processor. Prediction time based on the paralleled string kernel computing scheme shows 5–90 times faster performance than the CPU based implementation.
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Shi, Y., Ban, T., Guo, S., Xu, Q., Kadobayashi, Y. (2010). Fast Implementation of String-Kernel-Based Support Vector Classifiers by GPU Computing. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_18
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DOI: https://doi.org/10.1007/978-3-642-17534-3_18
Publisher Name: Springer, Berlin, Heidelberg
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