Parametric : SVM and neural networks are both parametric but for different reasons.
* For SVM the typical parameters are; soft-margin parameter (C), parameter of the kernel function (gamma).
* Neural networks also have parameters but it is a lot more than SVM. Some NN parameters are the number of layers and their size, number of training epochs, and the learning rate.
Embedding Non-Linearity : Both the methods can embed non-linear functions.
* SVM does this through the usage of kernel method.
* Neural Networks embed non-linearity using non-linear activation functions.
Comparable Accuracy :
* If both SVM and Neural Networks are trained in the same dataset, given the same training time, and the same computation power they have comparable accuracy.
* If neural networks are given as much computation power and training time as possible then it outperforms SVMs.
