I am working on a hybrid CNN-SVM where I aim to use CNN for feature extraction and SVM for classification. However, I am confused as after reading related works, I found many approaches:

-Some use SVM instead of the softmax layer.

-Some use SVM after flatten layer.

-Others replace the activation function from softmax to linear and the loss function from cross entropy to L1-SVM.

So I wanted to know which method is the correct one among these approaches.

  • $\begingroup$ Hello, First two methods seems to be correct. The first 2 seems to be very similar as softmax layer is applied after flattened layer only. So no matter you place SVM after flatten/linear layer or instead of softmax layer it will work similarly. Not sure about third if you could provide some reference it will be helpful $\endgroup$ Jan 14 at 16:48
  • $\begingroup$ @Ashwiniku918 Actually, I have tried the first two methods and they don't give me the same results (accuracy). When using an SVM classifier instead of a softmax layer, I obtained better results than after the flatten layer. Considering the third appoache here is a reference: scielo.org.mx/… $\endgroup$
    – root
    Jan 16 at 13:01

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