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I have tested the same data with my Matlab Code(all parameter(nu = 0.5, cache size, epsilon) the same) and It seems that the model is similar (same SVs and coefficients) expect the bias. Here they differ from -10.52 (Matlab) to -42.89 (Java). Furthermore, I have checked the output of the Kernels and they also seem to match perfectly, but because of the different biases, I have a 100% accuracy on my training data for Matlab, but only 50% in Java.
Therefore my questions, is there an error in my setup and have anyone encountered similar issues?
Does the libsvm code finish as soon as it reaches the 50% mark (nu)?
Best regards
Yannick
The text was updated successfully, but these errors were encountered:
Hey Everyone,
I'm currently working on One-Class SVMs in Java and I came across the following problem.
When I finish training and for whatever reason try to classify my training data, it appears that only half of the samples become classified correctly.
`
wekaSVM = new LibSVM();
wekaSVM.setKernelType(new SelectedTag(LibSVM.KERNELTYPE_RBF, LibSVM.TAGS_KERNELTYPE));
wekaSVM.setSVMType(new SelectedTag(LibSVM.SVMTYPE_ONE_CLASS_SVM, LibSVM.TAGS_SVMTYPE));
wekaSVM.buildClassifier(dataInst);
double[][] normData = wekaSVM.distributionsForInstances(dataInst);
`
I have tested the same data with my Matlab Code(all parameter(nu = 0.5, cache size, epsilon) the same) and It seems that the model is similar (same SVs and coefficients) expect the bias. Here they differ from -10.52 (Matlab) to -42.89 (Java). Furthermore, I have checked the output of the Kernels and they also seem to match perfectly, but because of the different biases, I have a 100% accuracy on my training data for Matlab, but only 50% in Java.
Therefore my questions, is there an error in my setup and have anyone encountered similar issues?
Does the libsvm code finish as soon as it reaches the 50% mark (nu)?
Best regards
Yannick
The text was updated successfully, but these errors were encountered: