Abstract
Support vector machine is an effective pattern classification method. Its time complexity and space complexity is 3 ( ) n and 2 ( ) n respectively for training samples with scale of n . Meanwhile, for core vector machine, the relation between time complexity and the scale of training samples is linear and space complexity is independent with the scale of training samples. In this paper, for the problem of big data classification, the concept and principle of support vector machine are described and support vector machine is converted into the form of minimum enclosing ball, consequently core vector machine is used to efficiently obtain the approximate optimal solution. Experiments confirm that core vector machine algorithm can classify the big data quickly and efficiently.
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