# How to use libSVM for one-class SVM problems?

I plan to use libSVM for a one-class svm problem, but I'm not sure about the meaning of nu in svm_parameter.

Does it mean the probability that a test point lies outside of a set S (estimated from the training data) equals nu?

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Related to: SVM with only one type of label. –  chl Oct 28 '12 at 8:31
How did you set the label of training data and test data –  Mark.M Oct 31 '13 at 11:21

A Tutorial on $\nu$-Support Vector Machines [PDF] (Section 6, proposition 1) It's not exactly a probability. In the context of soft-margin SVM, we introduce slack variables in the margin and minimize its sum, and:
• "(i) $\nu$ is an upper bound on the fraction of margin errors (and hence also on the fraction of training errors).
• (ii) $\nu$ is a lower bound on the fraction of Support Vectors."