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一种新的支持向量分类算法ACNN-SVM 被引量：2

A New Algorithm of Classification Based on Support Vector ACNN-SVM
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摘要 针对NN-SVM算法的不足,提出了一种新的支持向量分类算法——ACNN-SVM.先对训练样本集进行最近邻修剪,用SVM训练得到一个SVM模型,然后,计算最近邻修剪后的训练样本集中样本到超平面的距离,如果距离差大于给定的阈值则将其从最近邻修剪后的训练样本集中删除,最后对再修剪后的样本集用SVM训练得到一个最终的SVM模型.实验表明,ACNN-SVM算法的效果优于NN-SVM算法. A new Support Vector algorithm of classification ACNN-SVM is put forward aiming at the deficiency of NN-SVM. Firstly, it prunes the training set according to whether it is the nearest neighbor or not and gets a SVM model. Secondly, the distance is calculated from each sample of the training samples set to its super-plane. The distance is deleted from the reserved training samples set if it is greater than the given threshold. A final SVM model is gotten through SVM training of the leaved samples set. The experiment shows that ACNN-SVM excels NN-SVM.
作者 业巧林 业宁 张训华 武波 宋爱美
机构地区 南京林业大学信息技术学院 山东科技大学信息科学与工程学院
出处 《郑州大学学报（理学版）》 CAS 2008年第3期56-58,共3页 Journal of Zhengzhou University:Natural Science Edition
基金 国家自然科学基金资助项目 编号30671639 江苏省自然科学基金资助项目 编号BK2005134
关键词 NN-SVM算法 ACNN-SVM算法 超平面距离 阈值 NN-SVM algorithm ACNN-SVM algorithm distance of the super plane threshold
分类号 TP391.8 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献7

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共引文献119

同被引文献19

  • 1杨绪兵,陈松灿.基于原型超平面的多类最接近支持向量机[J].计算机研究与发展,2006,43(10):1700-1705. 被引量：16
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引证文献2

二级引证文献9

郑州大学学报（理学版）
2008年 第3期

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