**4. Discussion**

We can make several conclusions from our completed simulations and experimental results. All of the presented points, except the last two, refer to research experiments with linear SVM classifier. The last two points refer to cases that include SVM classification with RBF kernel. The main points of this research paper are given, as follows:


classification: half of them in favor of linear decay scheduler, half of them in favor of cyclical learning rates. CLRs might be the right solution for experimental scenarios under a smaller ratio of the training set. Neural network fine-tuning with cyclical learning rates resulted in more stable training and, thus, less prone to overfitting compared to training with linear decay scheduler. In our experiments, we used a *triangular* policy for the CLRs, but it might be an option to use the *triangular2* policy. Whichever policy is implemented, it should provide the right balance between stability and accuracy of training.

