| 基于最小二乘法的无监督支持向量机 |
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| 引用本文： | 孔波,王红蔚.基于最小二乘法的无监督支持向量机[J].河南教育学院学报(自然科学版),2014(4):17-19. |
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| 作者姓名： | 孔波 王红蔚 |
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| 作者单位： | 河南教育学院数学与统计学院; |
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| 基金项目： | 河南省基础与前沿项目(122300410229);河南省教育厅科学技术重点研究项目(12B110005) |
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| 摘 要： | 将最小二乘支持向量机的思想引入无监督学习,提出一个最小二乘无监督支持向量机.首先假设超平面过样本中心点,再给出线性可分的条件构造目标函数和约束条件,从而得到一个线性规划问题去求解聚类问题.
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| 关 键 词： | 无监督学习 最小二乘支持向量机 线性规划 聚类 |
Unsupervised Support Vector Machine Based on the Least Square Method |
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| KONG Bo,WANG Hong-wei.Unsupervised Support Vector Machine Based on the Least Square Method[J].Journal of Henan Education Institute(Natural Science Edition),2014(4):17-19. |
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| Authors: | KONG Bo WANG Hong-wei |
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| Institution: | (School of Mathematics and Statistics, Henan Institute of Education, Zhengzhou 450046, China) |
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| Abstract: | Least square unsupervised support vector machines are presented by introducing the ideas of the least squares support vector machine（SVM）. It assumes that hyperplane over the center of the sample points and gives the conditions of linearly separable, so a linear program model to solve clustering problem by constructing a new ob- jective function and constraints, so that SVMs can be used to cluster the unlabeled data. |
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| Keywords: | unsupervised learning least squares support vector machines linear programming clustering |
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