Explanations of Theory and Performance of Support Vector Machine

Hitherto, data points are extremely popular in many difference branches of natural science. For this purpose, this is a mathematical article mainly focuses on how to separate or categorize data points efficiently based on their characteristics. The methods are the following. For the linear separable data, the most fundamental Support Vector Machine(SVM) model can be used, while for non-linear separable data, the slack variables and kernel tricks are two efficient techniques. To test whether there is a significant difference among kernels, two kernels, the Gaussian and polynomial kernels, are chosen. The results for those two are quite alike (used a group of data of iris to showcase this). Next, the author applies two kernels to categorize data points of breast cancer. The whole point of this is to separate data in the most efficient way. In this manner, researchers can better predict events, such as injuries or diseases, and take quicker actions. This paper highlights the importance of SVM model in dealing with data-driven problems.