Kernel SVM — Decision Boundary Visualization

Kernel:
RBFPolynomialLinear C (margin): 1.0
γ / degree: 1.0
Reset Data Train SVM Click canvas to add points (left=class A, right=class B) SV: — | Acc: —

Kernel SVM finds the maximum-margin hyperplane in feature space induced by the kernel K(x,x'). RBF kernel K=exp(-γ‖x-x'‖²) maps to infinite-dimensional space. C controls the regularization (large C = hard margin). Click canvas to add training points, then Train SVM. Right-click adds class B.