An effective real time update rule for improving performances both the classification and regression problems in kernel methods

It is general solution to get an answer from both classification and regression problem that information in real world matches matrices. This paper treats primary space as a real world, and dual space that primary spaces transfers to new matrices using kernel. In practical study, there are two kinds of problems, complete system which can get an answer using inverse matrix and ill-posed system or singular system which cannot get an answer directly from inverse of the given matrix. Furthermore, the problems are often given by the latter condition; therefore, it is necessary to find regularization parameter to change ill-posed or singular problems into complete system. This paper compares each performance under both classification and regression problems among GCV, L-curve, and kernel methods. This paper also suggests dynamic momentum which is learning under the limited proportional condition between learning epoch and the performance of given problems to increase performance and precision for regularization. Finally, this paper shows the results that suggested solution can get better or equivalent results compared with GCV, and L-curve through the experiments using Iris data which are used to consider standard data in classification, Gaussian data which are typical data for singular system, and Shaw data which is an one-dimension image restoration problem.