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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1$\begingroup$ Related to: SVM with only one type of label. $\endgroup$– chlOct 28, 2012 at 8:31
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$\begingroup$ How did you set the label of training data and test data $\endgroup$– Mark.MOct 31, 2013 at 11:21
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$\begingroup$ @Mark.M, What's the meaning of the labels vector in the case of One Class SVM? $\endgroup$– RoyiMar 23, 2017 at 20:43
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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."
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$\begingroup$ my training data consists of the instances from the class I want to identify, and I know most real-world data will NOT fall into that class. Should I set the value of nu to be smaller (e.g., less than 0.5?) $\endgroup$ Oct 28, 2012 at 18:14
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$\begingroup$ Well, that's a good question. What I didn't understand is how the information of the "real-world data" could change the way you set the "nu" parameter. As I see it, you need to expect that your training will generalize and, in that sense, the parameter nu is related to how many outliers are going to be permitted (or, it's a upper bound in the fraction of training points outside the estimated region). Nevertheless, a small "nu" indicates a bigger region to be estimated. (springerlink.com/content/978-3-540-34628-9/…) What you say may work in this sense. $\endgroup$– ZehSilvaOct 29, 2012 at 2:37