Support Vector Machines Principles and Actually Example
Xingyu Chen, Zewen Yin, Hao Tian
Abstract
With the development of big data technology, it is necessary to quickly and accurately mine the information contained in data. In this paper, we study the selection of kernel function types and the selection of kernel function parameters for support vector machines under classification and regression problems, and experimentally verify their regression prediction performance and classification performance on scientific datasets. Firstly, the scientific data mining model is constructed, the model design principle is clarified, and then linear kernel function, Gaussian kernel function, and polynomial kernel function are selected for the comparison experiments, and the performance classification and regression prediction of the data are finally obtained.