| 基于支持向量机的改进高斯核函数聚类算法研究 |
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| 引用本文： | 阎晓娜,赵犁丰. 基于支持向量机的改进高斯核函数聚类算法研究[J]. 现代电子技术, 2011, 34(13): 67-70,73 |
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| 作者姓名： | 阎晓娜 赵犁丰 |
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| 作者单位： | 中国海洋大学信息科学与工程学院电子工程系,山东青岛,266100 |
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| 基金项目： | 863项目,“基于ROV的黄色物质水下原位探测系统”(2008AA09Z105) |
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| 摘 要： | 针对基于支持向量机的聚类算法中，由于高斯核在无限远处的衰减几乎为零，从而影响聚类效果的问题，采用了改进的高斯核函数。该方法使在高维特征空间中，核函数不仅满足在测试点附近有较快的衰减速度，而且在无限远处仍能保持适度的衰减，从而提高聚类效果。实验表明，改进的高斯核比高斯核聚类错误率更低。
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| 关 键 词： | 改进的高斯核 聚类 SVC 高斯核 |
Clustering Algorithm of Improved Gauss Kernel Function Based on SVM |
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| YAN Xiao-na,ZHAO Li-feng. Clustering Algorithm of Improved Gauss Kernel Function Based on SVM[J]. Modern Electronic Technique, 2011, 34(13): 67-70,73 |
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| Authors: | YAN Xiao-na ZHAO Li-feng |
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| Affiliation: | YAN Xiao-na,ZHAO Li-feng(Department of Electronics Engineering,Ocean University of China,Qingdao 266100,China) |
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| Abstract: | Aiming at the problem that Gauss kernel in infinite distance attenuation is almost zero thus affects the clustering effect in the SVM-based clustering algorithm,an improved Gauss kernel function is adopted.The system makes kernel function not only satisfy a server decay rate in the test point nearby,but also keep modest attenuation in infinite distance for improving the clustering effect in high-dimension feature space.The experiments show that the improved Gauss kernel has a lower clustering error rate. |
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| Keywords: | improved Gauss kernel function clustering support vector clustering Gauss kernel |
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