a) It controls the size of the margin between the classes. 

b) It determines the number of misclassified training examples. 

c) It balances the tradeoff between maximizing the margin and minimizing errors.

d) I am not familiar with the role of the cost parameter. 

In R, the "e1071" package provides an implementation of SVMs.

In Python, the "scikit-learn" library offers an implementation of SVMs. 

Both packages provide easy-to-use functions to train SVM models with various kernels, Including linear, polynomial, radial basis function (RBF), and sigmoid. 

All of them.

a smaller margin and an increased number of support vectors.

a wider margin and an increased number of support vectors.

a smaller margin and a decreased number of support vectors.

a wider margin and a decreased number of support vectors.