Support Vector Machines based on Scaling kernels

机译：基于缩放内核的支持向量机

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摘要

Kinds of admissible support vector kernel called scaling kernels are presented in this paper. In fact, scaling kernels are the multi-dimensional scaling function with translation vectors and they are a set of complete basis in the sub-space of the square and integrable space. Hence, the goal of support vector machines (SVMs) based on scaling kernels is to find the optimal scaling coefficients in a scaling space. In term of theory, SVMs based on scaling kernels can approximate any objective function in some space by any precision. The results obtained by our simulations show the feasibility and validity of scaling kernels.

机译：本文提出了可允许的支持向量内核，称为缩放内核。实际上，缩放核是具有平移矢量的多维缩放函数，它们是平方和可积空间的子空间中的一组完整基础。因此，基于缩放内核的支持向量机（SVM）的目标是在缩放空间中找到最佳缩放系数。从理论上讲，基于缩放内核的SVM可以在任何空间以任何精度近似任何目标函数。通过仿真获得的结果表明了缩放内核的可行性和有效性。

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来源
《2002 6th International Conference on Signal Processing Proceedings (ICSP'02) Vol.2; Aug 26-30, 2002; Beijing, China》|2002年|p.1142-1145|共4页
会议地点 Beijing(CN);Beijing(CN)
作者
Li Zhang; Weida Zhou; Licheng Jiao;
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作者单位

National Key Lab for Radar Signal Processing, Xidian University, Xi'an, 710071, China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类无线电电子学、电信技术;
关键词
support vector machine; support vector kernel; scaling kernel; gaussian kernel;

机译：支持向量机;支持向量内核;缩放内核;高斯内核;

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Support Vector Machines based on Scaling kernels

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