I'm working with text recognition and currently I'm using support vector machine method. I would like to try with neural network also. I read a few documents about how neural network works, but the theory is quite heavy and I don't know exactly how will it apply to my case. So it would be good if someone can help me to make it clear, especially with the neural network's architecture.
Currently, in SVM, I have 200 features (divided into 4 main categories), which is used to recognize the text. If I move to neural network, With 200 features, does it mean that I will have 200 neurons in the input layer?
With 200 features, how will that result in the architecture of the neural network (in term of number of hidden layers and neurons in them)?
In SVM, I have two class classification (basically, true and false) and multi-class classification (labels), how this difference will apply to the output layer of the neural networks?
And I also have a few general questions :
- What will help to decide the number of hidden layers and the number of neurons inside each hidden layer?
- Does the number of hidden layers relate to the accuracy ?
I'm new to neural network so it would be great if you can explain to me in an understandable way. :) Thank you very much.