Abstract
In this paper, a new algorithm for training support vector machines (SVMs) for classification problems with parallel sequential minimal optimization (SMO) is proposed. The selection of the working set is paralleled so that the iteration of the optimization process is reduced greatly. The experimental results show the training time of the proposed method is always less than the original SMO algorithm, and at the same time the classification accuracy is kept.
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Wang, X., Guo, J. (2013). An Algorithm for Parallelizing Sequential Minimal Optimization. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_81
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DOI: https://doi.org/10.1007/978-3-642-42042-9_81
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