一种改进的局部多核学习算法

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l1范数形式,使用选通函数的lp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.

A Kind of Improved Localized Multiple Kernel Learning Algorithm

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 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.