Support Vector Machine, Kernel Trick, Svm, Soft Margin Svm, Kernel.
the distance as yi(w.xi+b) ≥ 1-𝜉ᵢ. The value of 𝜉ᵢ is the distance from the correct hyperplane in the incorrect direction. Now our equation can be summarized as, And it should ideally represent the number of “mistakes” the SVM makes. Therefore, our new objective function would be: Dual Form of SVM