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Parallel sequential minimal optimization for the training of support vector machines.

L J Cao·S S Keerthi·Chong-Jin Ong·J Q Zhang·Uvaraj Periyathamby·Xiu Ju Fu·H P Lee
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Abstract

Sequential minimal optimization (SMO) is one popular algorithm for training support vector machine (SVM), but it still requires a large amount of computation time for solving large size problems. This paper proposes one parallel implementation of SMO for training SVM. The parallel SMO is developed using message passing interface (MPI). Specifically, the parallel SMO first partitions the entire training data set into smaller subsets and then simultaneously runs multiple CPU processors to deal with each of the partitioned data sets. Experiments show that there is great speedup on the adult data set and the Mixing National Institute of Standard and Technology (MNIST) data set when many processors are used. There are also satisfactory results on the Web data set.

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AlgorithmsArtificial IntelligenceNumerical Analysis, Computer-AssistedJournal ArticleResearch Support, Non-U.S. Gov't

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L J Cao, S S Keerthi, Chong-Jin Ong, J Q Zhang, Uvaraj Periyathamby, Xiu Ju Fu, H P Lee. 2006. Parallel sequential minimal optimization for the training of support vector machines.. https://doi.org/10.1109/tnn.2006.875989

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