Refining Kernels for Regression and Uneven Classification Problems
Jaz Kandola, John S. Shawe-Taylor · 2003
Kernel alignment has recently been proposed as a method for measuring the degree of agreement between a kernel and a classification learning task. In this paper we extend the notion of kernel alignment to two other common learning problems: regression and classification with uneven data. We present a modified definition of alignment together with a novel theoretical justification for why improving alignment will lead to better performance in the regression case. Experimental evidence is provided to show that improving the alignment leads to a reduction in generalization error of standard regressors and classifiers.