Preselection of support vector candidates by relative neighborhood graph for large-scale character recognition
Preselection of support vector candidates by relative neighborhood graph for large-scale character recognition
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DOI：
10.1109/icdar.2015.7333773
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发表时间：
2015-08
期刊：
影响因子：
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通讯作者：
Masanori Goto;R. Ishida;S. Uchida
中科院分区：
文献类型：
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作者：
Masanori Goto;R. Ishida;S. Uchida
提出了一种用于训练大规模数据集的支持向量机的预选方法。具体地说，该方法选择类边界周围的模式，并将所选数据馈送给训练支持向量机。对于选择，即搜索边界模式，我们使用相对邻域图(RNG)。RNG对每一对相邻模式都有一条边，因此，我们可以通过寻找连接不同类别模式的边来找到边界模式。通过大规模手写数字模式识别实验，在不降低识别精度的情况下，本文提出的预选方法使支持向量机的训练速度提高了5~15倍。
We propose a pre-selection method for training support vector machines (SVM) with a large-scale dataset. Specifically, the proposed method selects patterns around the class boundary and the selected data is fed to train an SVM. For the selection, that is, searching for boundary patterns, we utilize a relative neighborhood graph (RNG). An RNG has an edge for each pair of neighboring patterns and thus, we can find boundary patterns by looking for edges connecting patterns from different classes. Through large-scale handwritten digit pattern recognition experiments, we show that the proposed pre-selection method accelerates SVM training process 5-15 times faster without degrading recognition accuracy.