Maximum margin hyperplane · support vectors · RBF kernel · soft margin C
Class +1
Class −1
Support vector
SVM finds the hyperplane maximizing the margin = 2/‖w‖ between classes. Support vectors are the points closest to the boundary — only they determine the hyperplane. The kernel trick maps data to high-dimensional space implicitly: K(x,x') = φ(x)·φ(x'). RBF kernel: K(x,x') = exp(-γ‖x-x'‖²). Soft margin C controls the trade-off between margin width and classification error.