| 基于k-最近邻的支持向量预选取方法 |
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| 引用本文： | 韩德强,韩崇昭,杨艺.基于k-最近邻的支持向量预选取方法[J].控制与决策,2009,24(4). |
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| 作者姓名： | 韩德强 韩崇昭 杨艺 |
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| 作者单位： | 西安交通大学,电信学院,西安,710049 |
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| 基金项目： | 国家自然科学基金，国家重点基础研究发展规划(973计划) |
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| 摘 要： | 在所有的训练样本中只有支持向量(SVs)能对支持向量机分界面优化结果产生显著影响.基于七一最近邻规则.提出了一种训练样本的预选取方法.针对一些典型人工数据集、公用基准数据集以及TM遥感数据的实验结果表明.该方法能够有效减少训练样本数目.显著加快学习速度,并保证理想的分类精度.
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| 关 键 词： | 支持向量机 样本预选取 k-最近邻 模式分类 |
Approach for pre-extracting support vectors based on k-NN |
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| HAN De-qiang,HAN Chong-zhao,YANG Yi.Approach for pre-extracting support vectors based on k-NN[J].Control and Decision,2009,24(4). |
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| Authors: | HAN De-qiang HAN Chong-zhao YANG Yi |
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| Abstract: | In support vector machine(SVM) only support vectors (SVs) have the significant influence on the optimization result. An approach for pre-extracting SVs based on k-NN is proposed. The experimental results based on some artificial datasets, some real-world datasets and TM remote sensing dataset show that the approach proposed can effectively reduce the size of training sets and accerlerate the learning speed. At same time, the classification accuracies are ensured. |
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| Keywords: | SVM Sample pre-extracting k-NN Pattern classification |
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