Non-flat Function Representation with Scale-tunable Kernel Model
Based on orthogonal least square algorithm(OLS),a new type of scale-tunable kernel model is proposed to enhance the sparseness of representation for non-flat functions.At each regressor stage,the scale of each term is selected by minimizing the residual using group search optimizer.Experimental results indicate that the new method can produce a kernel function model with better sparseness and generalization than the traditional fix-scale kernel models.