| 基于取样的潜在支持向量机序列最小优化算法 |
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| 引用本文： | 鲁淑霞,曹贵恩,孟洁,王华超.基于取样的潜在支持向量机序列最小优化算法[J].河北大学学报(自然科学版),2011,31(2):113-117. |
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| 作者姓名： | 鲁淑霞 曹贵恩 孟洁 王华超 |
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| 作者单位： | 河北大学,数学与计算机学院,河北,保定,071002 |
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| 基金项目： | 河北省自然科学基金资助项目，河北大学自然科学研究计划博士项目，河北省教育厅科学技术研究计划资助项目 |
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| 摘 要： | 为了提高潜在支持向量机求解大规模问题的训练速度,提出了基于样本取样的潜在支持向量机序列最小优化算法,去掉了大部分非支持向量,把支持向量逐渐压缩到取样样本集中.此算法特别适合大样本数据且支持向量个数相对较少的情况.实验表明,改进的序列最小优化算法加速了潜在支持向量机分类器训练时间.
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| 关 键 词： | 潜在支持向量机 序列最小优化 取样 |
A Sequential Minimal Optimization Algorithm for the Potential Support Vector Machine Based on Sampling |
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| LU Shu-xia,CAO Gui-en,MENG Jie,WANG Hua-chao.A Sequential Minimal Optimization Algorithm for the Potential Support Vector Machine Based on Sampling[J].Journal of Hebei University (Natural Science Edition),2011,31(2):113-117. |
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| Authors: | LU Shu-xia CAO Gui-en MENG Jie WANG Hua-chao |
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| Institution: | (College of Mathematics and Computer science,Hebei University,Baoding 071002,China) |
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| Abstract: | To accelerate the training speed of the Potential Support Vector Machine(PSVM)for large-scale datasets,a new method is proposed,which introduces the sequential minimal optimization(SMO)algorithm based on sampling for PSVM.The new method removes most non-support vectors,and compresses the support vectors to the sampling set.This method is more suitable for large-scale datasets with relatively small number of support vectors.The experimental results show that the improved SMO algorithm decreases the training time. |
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| Keywords: | potential support vector machine sequential minimal optimization sampling |
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