重庆工商大学学报（自然科学版）

引用本文:	丁跃.一种改进的局部多核学习算法(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2014，31（11）：56-61
	CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435

【打印本页】【下载PDF全文】【查看/发表评论】【EndNote】【RefMan】【BibTex】

←前一篇|后一篇→

过刊浏览高级检索

本文已被：浏览 917次下载 1574次	码上扫一扫！
分享到：微信更多字体:加大+\|默认\|缩小-
一种改进的局部多核学习算法
丁跃¹
重庆大学数学与统计学院，重庆 401331

摘要:

局部多核学习算法（LMKL）是一种变系数的多核支持向量机算法，其利用选通函数局部的选取合适的合成核函数；但是其选通函数有严重的参数沉余的问题，为此提出了改进的局部多核学习算法（ILMKL），在其目标函数中加入正则项，区别于LMKL中选通函数的l1范数形式，使用选通函数的lp范数形式，增强核函数间的“互补”作用；采用该算法在模拟数据集和UCI数据集上实验，结果表明该算法取得较高的分类能力。

关键词: 支持向量机局部学习多核学习正则化

DOI：

分类号:

基金项目:

A Kind of Improved Localized Multiple Kernel Learning Algorithm

DING Yue

Abstract:

Localized Multiple Kernel Learning (LMKL) Algorithm is a kind of multiple kernel support vector machine algorithm with varying coefficient and uses gating function to locally select suitable compound kernel function, however, its gating function has serious parameter redundancy, therefore, this paper proposes improved localized multiple kernel learning (ILMKL) algorithm, adds regularization term to objective function to discriminate l1 norm form of the gating function in LMKL, uses lp norm form of gating function to enhance “complementary” role between kernel functions, uses this algorithm to conduct experiments on simulation data set and UCI data set and draws the conclusion that this algorithm gets higher classification capacity. 

Key words: support vector machine locally learning multiple kernel learning regularization