Support Vector Machines

#Learning Objectives

• Implement a linear SVM from scratch using hinge loss and gradient descent on the primal formulation
• Explain the maximum margin principle and identify support vectors from a trained model
• Compare linear, polynomial, and RBF kernels and explain how the kernel trick avoids explicit high-dimensional mapping
• Evaluate the tradeoff controlled by the C parameter between margin width and classification errors