Different types of SVM -- opencv

They are different formulations of SVM. At the heart of SVM is an mathematical optimization problem. This problem can be stated in different ways.

C-SVM uses C as the tradeoff parameter between the size of margin and the number of training points which are misclassified. C is just a number, the useful range depends on the dataset and it can range from very small (like 10-5) to very large (like 10^5), depending on your data.

nu-SVM uses nu instead of C. nu is roughly a percentage of training points which will end up as support vectors. The more support vectors, the wider your margin is, the more training points which will be misclassified. nu ranges from 0.1 to 0.8 - at 0.1 roughly 10% of training points will be support vectors, at 0.8, more like 80%. I say roughly because its just correlated that way - its not exact.

epsilon-SVR and nu-SVR use SVM for regression. Instead of doing binary classification by finding a maximum margin hyperplane, instead the concept is used to find a hypertube which best fits the data in order to use it to predict future models. They differ in the way they are parameterized (like nu-SVM and C-SVM differ).

One-Class SVM is novelty detection. Rather than binary classification, or predicting a value, instead you give the SVM a training set and it attempts to train a model to wrap around that set so that a future instance can be classified as part of the class or outside the class (novel or outlier).