Est. Budget: $60.00
I have a data set (not large, have around 8000 observations). Need to finish the following work using Matlab or R language:
1. use support vector machine (basic SVM, i.e., linear kernel).
2. Use k-fold cross-validation to evaluate the performance of SVM.
3. Evaluate the performance of SVM by tuning parameter C (slack penalty).
4. Use cross-validation on training dataset to pick the best C.
5. Apply kernel SVM with RBF kernel. Using default parameter settings, get k-fold cross-validation ...