Support Vector Machine(SVM)is a powerful instrument for solving pattern classification problem, but it is not suitable for large-scale data, due to the drawbacks of slow training speed, large computational cost and low generalization.An accurate support vector machine algorithm is proposed, which uses training samples lying close to the separation boundary. First of all, K-means clustering is performed to the initial training data, and then the boundary samples are selected in each cluster by K-nearest neig...
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