MP-polynomial kernel for training support vector machines

被引：0

作者：

Mejia-Guevara, Ivan ^{[1
]}

Kuri-Morales, Angel ^{[2
]}

机构：

[1] Univ Nacl Autonoma Mexico, IIMAS, Circuito Escolar S-N,CU, Mexico City 04510, DF, Mexico

[2] Inst Tecnolog Autonomo Mexico, Dept Comp, Mexico City 01000, DF, Mexico

来源：

PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS AND APPLICATIONS, PROCEEDINGS | 2007年 / 4756卷

关键词：

MP-polynomial kernel; kernel methods; support vector machine;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this article we present a new polynomial function that can be used as a kernel for Support Vector Machines (SVMs) in binary classification and regression problems. We prove that this function fulfills the mathematical properties of a kernel. We consider here a set of SVMs based on this kernel with which we perform a set of experiments. Their efficiency is measured against some of the most popular kernel functions reported in the past.

引用

页码：584 / +

页数：2

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