4. Which of the following is/are true regarding SVMa?

(a) For two dimensional data points, the separating hyperplane learned by a linear SVM will be a straight line.

(b) In theory, a Gaussian kernel SVM can model any complex separating hyperplane.

(c) The tradeoff between misclassification and simplicity of the model is determined by the cost parameter C.

(d) Overfitting in an SVM is a function of number of support vectors.

(e) All of the above