Support Vector Machine, Kernel Trick, Svm, Soft Margin Svm, Kernel.
penalizing factor to our objective function. If the datapoint is within the boundary then the penalizing factor 𝜉ᵢ is 0. Else this value will be equal to the distance from the correct hyperplane. In hard margin SVM we had the constraint that yi(w.xi+b) ≥ 1 but in soft margin SVM we are representing