Kernel optimization approach based on maximumsubclass margin criterion
最大子分类间隔准则的核函数优化

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.