Novel method of mining classification information for SVM training |
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Authors: | Fengshan Shen Junying Zhang Xiguo Yuan |
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Institution: | SHEN Fengshan,ZHANG Junying,YUAN Xiguo School of Computer Science and Engineering,Xidian University,Xi'an 710071,Shaanxi,China |
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Abstract: | Support vector machine (SVM) is an important classification tool in the pattern recognition and machine learning community,
but its training is a time-consuming process. To deal with this problem, we propose a novel method to mine the useful information
about classification hidden in the training sample for improving the training algorithm, and every training point is assigned
to a value that represents the classification information, respectively, where training points with the higher values are
chosen as candidate support vectors for SVM training. The classification information value for a training point is computed
based on the classification accuracy of an appropriate hyperplane for the training sample, where the hyperplane goes through
the mapped target of the training point in feature space defined by a kernel function. Experimental results on various benchmark
datasets show the effectiveness of our algorithm. |
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Keywords: | support vector machine(SVM) classification information incremental training candidate support vector |
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