k times Markov sampling for SVMC
k times Markov sampling for SVMC
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SVMC 的 k 次马尔可夫采样
DOI：
10.1109/tnnls.2016.2609441
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通讯作者：
Xinge You
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作者：
Bin Zou;Chen Xu;Yang Lu;Y Y Tang;Jie Xu;Xinge You
支持向量机（SVM）是分类问题中应用最广泛的学习算法之一。虽然支持向量机在实际应用中具有良好的性能，但由于训练样本的规模较大，算法复杂度较高。本文介绍了基于k次马尔可夫抽样的支持向量机分类算法，并对k次马尔可夫抽样支持向量机在基准数据集上的学习性能进行了数值研究。实验结果表明，与经典的SVM算法和已有的基于马尔可夫抽样的SVM算法相比，采用k次马尔可夫抽样的SVM算法不仅具有更小的误分类率、更少的抽样和训练时间，而且得到的分类器更稀疏。本文还讨论了k次马尔可夫抽样支持向量机在训练样本不平衡和大规模训练样本情况下的性能。
Support vector machine (SVM) is one of the most widely used learning algorithms for classification problems. Although SVM has good performance in practical applications, it has high algorithmic complexity as the size of training samples is large. In this paper, we introduce SVM classification (SVMC) algorithm based on k-times Markov sampling and present the numerical studies on the learning performance of SVMC with k-times Markov sampling for benchmark data sets. The experimental results show that the SVMC algorithm with k-times Markov sampling not only have smaller misclassification rates, less time of sampling and training, but also the obtained classifier is more sparse compared with the classical SVMC and the previously known SVMC algorithm based on Markov sampling. We also give some discussions on the performance of SVMC with k-times Markov sampling for the case of unbalanced training samples and large-scale training samples.