| 一种稀疏最小二乘支持向量机 |
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| 引用本文： | 赵会,黄景涛.一种稀疏最小二乘支持向量机[J].计算机工程与应用,2009,45(26):40-42. |
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| 作者姓名： | 赵会 黄景涛 |
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| 作者单位： | 河南科技大学电子信息工程学院,河南洛阳,471003 |
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| 基金项目： | 河南省重点科技攻关项目，河南科技大学青年基金 |
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| 摘 要： | 针对最小二乘支持向量机缺乏稀疏性的问题,提出了一种基于边界样本的最小二乘支持向量机算法。该算法利用中心距离比来选取支持度较大的边界样本作为训练样本,从而减少了支持向量的数目,提高了算法的速度。最后将该算法在4个UCI数据集上进行实验,结果表明:在几乎不损失精度的情况下,可以得到稀疏解,且算法的识别速度有了一定的提高。
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| 关 键 词： | 稀疏性 最小二乘支持向量机 中心距离比 边界样本 |
| 收稿时间： | 2009-1-7 |
| 修稿时间： | 2009-3-11
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Sparse least squares support vector machine |
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| ZHAO Hui,HUANG Jing-tao.Sparse least squares support vector machine[J].Computer Engineering and Applications,2009,45(26):40-42. |
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| Authors: | ZHAO Hui HUANG Jing-tao |
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| Affiliation: | ZHAO Hui,HUANG Jing-tao College of Electronic , Information Engineering,Henan University of Science , Technology,Luoyang,Henan 471003,China |
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| Abstract: | To solve the problem of sparseness lacking in the Least Squares Support Vector Machine(LS-SVM),a new least squares support vector machine based on the boundary samples is proposed,which uses center distance ratio to select bigger support value boundary samples and making them as training samples.Thus,the number of support vector is reduced and the speed of computing is improved.Finally,the new algorithm is tested on the four benchmarking UCI datasets.The result shows that the proposed algorithm can adaptive... |
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| Keywords: | sparseness least squares support vector machine center distance ratio boundary sample |
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