在分析现有基于经验特征空间核函数优化方法局限性的基础上,提出一种基于最大子分类间隔准则的核函数优化方法。该方法首先建立最大子分类间隔准则,然后结合数据在经验特征空间中的特点给出样本数据的类间散布矩阵和类内散布矩阵的表达式,最后利用奇异值分解实现核函数参数的优化选取。本文利用UCI(University of California, Irvine)数据对算法进行仿真实验,仿真结果表明了本文方法的正确性和有效性。

In order to deal with the kernel optimization, a new kernel data-dependent optimizaition kernel approach based on maximum subclass margin criterion is proposed. In this scheme, a maximum subclass margin function is created firstly. Then, the in-between-subclass and inter-subclass scatter matrix in the empirical feature space are defined. Finally, the optimal coefficients vector is solved by the selected optimization criterion. Experimental results based on UCI data show that it is effective and feasible.