| 支持向量机的研究 |
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| 引用本文： | 任丽晔,王静,关秀丽. 支持向量机的研究[J]. 长春大学学报, 2013, 0(12): 1595-1598 |
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| 作者姓名： | 任丽晔 王静 关秀丽 |
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| 作者单位： | [1]长春大学电子信息工程学院,长春130022 [2]吉林工业职业技术学院商学院,吉林吉林132000 |
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| 摘 要： | 支持向量机可以引入特征变换将原空间的非线性问题转化为新空间的线性问题。本文在论述支持向量机模型创建的基础上，着重对核函数的选取及参数的确定进行了研究，通过实验数据表明，文中创建的组合核函数，在人体下肢动作模式识别中，有较高的识别率。
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| 关 键 词： | 支持向量机 核函数 模式识别 |
Research on the Application of Support Vector Machine |
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| REN Li-ye,WANG Jing,GUAN Xiu-li. Research on the Application of Support Vector Machine[J]. Journal of Changchun University, 2013, 0(12): 1595-1598 |
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| Authors: | REN Li-ye WANG Jing GUAN Xiu-li |
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| Affiliation: | 1. College of Electronic Information Engineering, Changchun University, Changchun 130022, China; 2. Business School, Jilin Vocational College of Industry and Technology, Jilin 132000, China) |
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| Abstract: | Support vector machine （ SVM） can convert the nonlinear problem of the original space into the linear problem of new space by introducing feature transform. Based on discussing the model creation of SVM, this paper mainly studies the selection of kernel function and the determination of parameters. The experimental data shows that the combined kernel function created in this paper has higher recognition rate in human lower limb motion pattern recognition. |
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| Keywords: | SVM kernel function pattern recognition |
| 本文献已被 CNKI 维普 等数据库收录！ |
