Weighted p-norm distance t kernel SVM classification algorithm based on improved polarization.

ABSTRACT: The kernel function in SVM enables linear segmentation in a feature space for a large number of linear inseparable data. The kernel function that is selected directly affects the classification performance of SVM. To improve the applicability and classification prediction effect of SVM in different areas, in this paper, we propose a weighted p-norm distance t kernel SVM classification algorithm based on improved polarization. A t-class kernel function is constructed according to the t distribution probability density function, and its theoretical proof is presented. To find a suitable mapping space, the t-class kernel function is extended to the p-norm distance kernel. The training samples are obtained by stratified sampling, and the affinity matrix is redefined. The improved local kernel

SUBMITTER: Liu W 

PROVIDER: S-EPMC9008017 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature