SVM is the most commonly used machine learning algorithms, due to its wide range of functionalities and parameters.
There is a C parameter (Regularization parameter) in SVM, which can take any positive value. It is quite hard to choose that value correctly.
SVM can also take a new regularization parameter nu. This parameter is:
Relationship between C and nu
The relation between C and nu is represented by the following formula:
nu = A+B/C
Here A and B are constants
The C and nu parameters in SVM are equivalent to their classification power. The regularization in terms of nu is easier to evaluate as compared to C, but the nu SVM is usually harder to optimize, and runtime doesn't scale as well as the C variant with a number of input samples.