I want to classify using libsvm. I have 9 training sets , each set has 144000 labelled instances , each instance having a variable number of features. It is taking about 12 hours to train one set ( ./
I am now using libsvm for support vector machine classifier with Gaussian kernal. In its website, it provides a python script grid.py to select the best C and gamma.
How can I call two C applications from within another C application? e.g. : pg1.c can be run as ./a.out pg1_args
Is there any script that would transform a tab delimited data file into libSVM data format? For an example my unlabelled data:
According to this FAQ the model format in libsvm should be straightforward. And in fact it is, when I call just svm-train. As an example, the first SV for the a1a dataset is
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