| 基于分块回归的SVM逼近方法 |
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| 引用本文： | 王勇,吕显瑞,严然,苏立明.基于分块回归的SVM逼近方法[J].吉林工学院学报,2008,29(6):601-606. |
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| 作者姓名： | 王勇 吕显瑞 严然 苏立明 |
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| 作者单位： | [1]长春工业大学基础科学学院,吉林长春130012 [2]吉林大学数学研究所,吉林长春130012 [3]中国人民解放军91039部队,北京102401 |
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| 基金项目： | 国家自然科学基金资助项目
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| 摘 要： | 通过将海量的样本集合合理地分为数目比较小的几个子集，并在每个子集上分别作回归或逼近，使得训练SVM所需二次规划问题的维数大大降低。这样大大降低了训练SVM的运算量，同时提高了局部逼近和预测的能力，为SVM在回归或预测中的实时应用创造了条件。
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| 关 键 词： | SVM 回归 预测 分块 二次规划问题 |
SVM approaching method based on the subset regression |
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| WANG Yong,LV Xian-rui,YAN Ran,SU Li-ming.SVM approaching method based on the subset regression[J].Journal of Jilin Institute of Technology,2008,29(6):601-606. |
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| Authors: | WANG Yong LV Xian-rui YAN Ran SU Li-ming |
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| Institution: | 1.School of Basic Sciences;Changchun University of Technology;Changchun 130012;China;2.Institute of Mathematics;Jilin University;3.PLA No.91039;Beijing 102401;China |
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| Abstract: | By dividing the large number of samples into some subsets reasonably,we make the regression and approach in the subsets to greatly decrease the dimensions needed by the quadratic programming problems in SVM training.The method reduces the calculations in SVM training,improves the ability of local approaching and prediction and offers a good conditions for the application of SVM in regression and prediction. |
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| Keywords: | SVM |
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