relation: https://episodesplatform.eu/eprints/1329/
title: An iterative modified kernel based on training data
creator: Zhou, Zhi-xiang
creator: Han, Feng-qing
subject: Method and procesing
description: To improve performance of a support vector regression, a new method for a modified kernel function is proposed. In this method, information of all samples is included in the kernel function with conformal mapping. Thus the kernel function is data-dependent. With a random initial parameter, the kernel function is modified repeatedly until a satisfactory result is achieved. Compared with the conventional model, the improved approach does not need to select parameters of the kernel function. Simulation is carried out for the one-dimension continuous function and a case of strong earthquakes. The results show that the improved approach has better learning ability and forecasting precision than the traditional model. With the increase of the iteration number, the figure of merit decreases and converges. The speed of convergence depends on the parameters used in the algorithm.
publisher: Springer Verlag
date: 2009
type: Article
type: PeerReviewed
identifier:   Zhou, Zhi-xiang and Han, Feng-qing  (2009) An iterative modified kernel based on training data.  Applied Mathematics and Mechanics, 30 (1).  pp. 121-128. DOI: https://doi.org/10.1007/s10483-009-0113-x <https://doi.org/10.1007/s10483-009-0113-x>     
relation: http://dx.doi.org/10.1007/s10483-009-0113-x
relation: doi:10.1007/s10483-009-0113-x