I have seen people have put a lot of efforts on SVM and Kernels, and they look pretty interesting as a starter in Machine Learning. But if we expect that almost-always we could find outperforming solution in terms of (deep) Neural Network, what is the meaning of trying other methods in this era?
Here is my constraint on this topic.
- We think of only Supervised-Learnings; Regression, and Classification.
- Readability of the Result is not counted; only the Accuracy on the Supervised-Learning Problem counts.
- Computational-Cost is not in consideration.
- I am not saying that any other methods are useless.