n-class classification (n>=2), allows imperfect separation of classes with penalty multiplier C for outliers

n-class classification with possible imperfect separation. Parameter nu (in the range 0..1, the larger the value, the smoother the decision boundary) is used instead of C

one-class SVM. All the training data are from the same class, SVM builds a boundary that separates the class from the rest of the feature space

Regression. The distance between feature vectors from the training set and the fitting hyper-plane must be less than p. For outliers the penalty multiplier C is used

Regression; nu is used instead of p.