Use SVM model trained in Matlab for classification in python
I guess you understand how SVM works, so what I would do is to train the model again in python just on the support vectors you found rather than on all the original training data and the result should remain the same (as if you trained it on the full data), since the support vectors are the "interesting" vectors in the data, that are sitting on the boundaries.
I have a SVM model trained in MATLAB (using 6 features) for which I have:

Support Vectors [337 x 6]
Alpha [337 x 1]
Bias
Kernel Function: @rbf_kernel
Kernel Function Args = 0.9001
GroupNames [781 x 1]
Support Vector Indices [337 x 1]
Scale Data containing:


shift [1 x 6]
scale factor [1 x 6]


These above are all data that I am able to load in python.
Now I would like to use this model in python without retraining to perform classification in python. In particular I would like to create a SVM model in python from the support vector generated in MATLAB
Is it possible? How? Any help would be very appreciated!
I can't retrain it in python because I don't have the training data (and labels) anymore.