# One class classifier Cross validation

I am working on a problem which requires one-class classifier. I am using LIBSVM. I know there are tons of material out there but still I could not find the answer to my query.

1. How do I estimate the optimum parameters for the RBF kernel?

2. Using svm-train -s 2 -t 2 -v 5 train.scale, I am getting 49% accuracy and my training set has no outlier data. So, does this actually mean 98% accuracy in real world scenario?

• @gung this is a command line call. – Marc Claesen May 8 '14 at 13:47
• @gung the C++ tag is not relevant, I will remove it shortly. The libsvm library is written in C++ and has a command line interface (which is what is being used based on OP). The libsvm library is what is used behind the scenes in most SVM packages in higher languages like Python and R (e.g. in kernlab, e1707, scikit-learn, ...). – Marc Claesen May 8 '14 at 13:51
• @MarcClaesen, I think I see, so the idea is you would instal this, open cmd (in Windows) & run it from the prompt? (Clearly, I don't use libsvm...) – gung May 8 '14 at 13:59
• @gung yes, that's the idea. – Marc Claesen May 8 '14 at 14:03
• I have read lots of blogs about the parameter selection. Cross validation is suggested by most of people. Come on, how can you use cross vlaidation in training stage with only one class samples available? The classical one class classification problem is that only one class information is given in training stage. You can not calculate the error rate with anomaly samples!! – user164348 Jun 7 '17 at 11:30