Abstract
Sequential minimal optimization (SMO) algorithm is widely used for solving the optimization problem during the training process of support vector machine (SVM). However, the SMO algorithm is quite time-consuming when handling very large training sets and thus limits the performance of SVM. In this paper, a parallel implementation of SMO algorithm is designed with OpenMP, basing on the running time analysis of each function in SMO. Experimental results show that the performance for training SVM had been improved with parallel SMO when dealing with large datasets.
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