本文已被：浏览 1382次 下载 3211次
Received:December 02, 2016
Received:December 02, 2016
中文摘要: 支持向量机的核函数类型分为两类：局部核函数和全局核函数.局部核函数的值只受到相距很近数据点的影响，有很好的学习能力.全局核函数的值会受到距离较远数据点的影响，有很好的推广泛化能力.针对局部核函数学习能力良好但泛化能力差的缺点，提出一种结合局部核函数和全局核函数构造新联合函数的方法.实验结果表明，与局部核函数和全局核函数相比，新联合核函数有更好的预测能力，并且能够适应增量学习的过程.
Abstract:The Kernel function to support vector machines can be divided into two types: the local kernel function and the global kernel function. Because the local kernel function has excellent learning ability, but its generalization ability is limited, we structure a joint kernel function with two kinds of functions, so that it can combine the advantages of the two kinds of kernel functions. The experiment proves that the joint kernel function can adapt to the incremental learning process and it has better performance.
keywords: support vector machine incremental learning global kernel function local kernel function joint kernel function
文章编号： 中图分类号： 文献标志码：
基金项目:
引用文本：
李村合,马敏敏.增量支持向量机核函数的优化.计算机系统应用,2017,26(8):284-287
LI Cun-He,MA Min-Min.Optimization of Kernel Function in Incremental Support Vector Machine.COMPUTER SYSTEMS APPLICATIONS,2017,26(8):284-287
李村合,马敏敏.增量支持向量机核函数的优化.计算机系统应用,2017,26(8):284-287
LI Cun-He,MA Min-Min.Optimization of Kernel Function in Incremental Support Vector Machine.COMPUTER SYSTEMS APPLICATIONS,2017,26(8):284-287
