Autocorrelation Kernel Functions for Support Vector Machines
Autocorrelation Kernel Functions for Support Vector Machines
复制标题
支持向量机的自相关核函数
DOI：
10.1109/icnc.2007.276
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Bing Zhang
中科院分区：
文献类型：
--
作者：
Rui Kong;Bing Zhang
核函数是支持向量机的关键部分，也是支持向量机的难点。研究了核函数与非线性映射和映射空间的关系。提出了一种新的容许支持向量机核。它是自相关核。从理论上证明了自相关函数是支持向量机的容许核函数。一些实验也表明了自相关核在分类和回归中的有效性。
Kernel functions (kernel) are key part and the hard issue of support vector machines. We research the relation of kernel functions and nonlinear mappings and mapped spaces. A new kind of admissible support vector machines kernel is presented. It is autocorrelation kernel. The theory proofs certify that autocorrelation functions are admissible support vector machines kernel. Several experiments also showed the validity of the autocorrelation kernel in classification and regression.