最优双核复合分类算法的构造

 由于使用单一且固定的核函数,传统的核分类算法不能有效地适应复杂的数据集合,导致分类性能下降.本文提出一种基于双核复合的分类算法ODKC(Optimal Double-Kernel Combination)的构造框架,通过融合两个基本核函数的映射来构造目标核函数.研究了双核复合的三种典型方式,并把这三种复合方式纳入到统一的框架下处理.论文以核与数据的匹配性度量KTA(Kernel Target Alignment)以及分类性能验证了所提算法的有效性.

Optimal Double-Kernel Combination Method for Classification

Traditional kernelised classification methods could not perform well sometimes because of using a single and fixed kernel,especially on some complicated data sets.In this paper,a novel optimal double-kernel combination(ODKC) method is proposed for complicated classification tasks.Firstly,data are mapped by two basic kernels into different feature spaces respectively,and then three kinds of optimal composite kernels are constructed by integrating information of the two feature spaces.Comparative experiments demonstrate the effectiveness of our methods.