Effects of kernel function on Nu support vector machines in extreme cases
Effects of kernel function on Nu support vector machines in extreme cases
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DOI：
10.1109/tnn.2005.860832
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发表时间：
2006
影响因子：
--
通讯作者：
K. Ikeda
中科院分区：
文献类型：
--
作者：
K. Ikeda
如何选择支持向量机的核函数是一个重要而又困难的问题。本文讨论了归一化特征向量的一种变体/spl nu/-SVM的解在两种极端情况下的性质：所有特征向量几乎正交和所有特征向量几乎相同。在前一种情况下，/spl nu/-SVM的解接近于所给示例的重心，而在后一种情况下，其解近似于具有线性核的/spl nu/-SVM的解。虽然在实践中没有使用极端核函数，但分析核函数对泛化性能的影响是有帮助的。
How we should choose a kernel function in support vector machines (SVMs), is an important but difficult problem. In this paper, we discuss the properties of the solution of the /spl nu/-SVM's, a variation of SVM's, for normalized feature vectors in two extreme cases: All feature vectors are almost orthogonal and all feature vectors are almost the same. In the former case, the solution of the /spl nu/-SVM is nearly the center of gravity of the examples given while the solution is approximated to that of the /spl nu/-SVM with the linear kernel in the latter case. Although extreme kernels are not employed in practice, analyzes are helpful to understand the effects of a kernel function on the generalization performance.