Open AccessJournal Article
Fast Classification Algorithm for Polynomial Kernel Support Vector Machines
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TL;DR: A fast classification algorithm for polynomial kernel support vector machines is presented, which expands the decision function of SVM into polynomials, and classifies new patterns by calculating the polynOMials’ value.
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Abstract: When the number of support vectors is large, the classification speed of a kernel function based on support vectors classifier is inevitably very slow in test phase, as it need to perform the computation between each support vector and the classified vector. To address this, a fast classification algorithm for polynomial kernel support vector machines is presented, which expands the decision function of SVM into polynomials, and classifies new patterns by calculating the polynomials’ value. The computational requirement of the algorithm is independent of the number of the support vectors, while the solution otherwise is unchanged. When the degree of the polynomial kernel or the dimension of the input space is small, the classification speed of this algorithm is much faster than the standard SVM classification method. The efficiency of this algorithm is also verified by the experiment result with real-world data set.
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