where 𝒘 is the normal vector to the hyperplane and 𝑏 controls the offset of the hyperplane to the origin. Besides linear SVM, Guyon et al. applied the kernel trick to SVM to construct nonlinear hyperplanes. Furthermore, Drucker et al. proposed Support Vector Regression (SVR), extending SVM to regression problems in supervised learning.