Vote Parallel SVM: An Extension of Parallel Support Vector Machine

Support Vector Machine (SVM) is a set of machine learning algorithms, which has been widely used in diverse domains. With the increasing size of datasets, the traditional SVM training algorithms for large-scale datasets become infeasible. Mathematical optimization and cascade parallelism are both popular strategies for accelerating SVM training. In these parallel methods, the use of appropriate parallel framework to reduce SVM training time has become a priority issue and a research focus. In this paper, we investigate and overview mathematical optimization algorithms and parallel technologies of SVM, and summarize parallel SVM solutions and application problems under different frameworks. We propose a Vote Parallel SVM to reduce the training time. Finally, we show experimental results comparing with baseline methods.

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