Choose the best classifier to predict the label of strings of a regular language I have to tackle this problem:
I have some strings that are my training set. These strings belong to a regular language corresponding to a deterministic finite automata (hidden namely I don't now it, neither the language nor the automata). A string is labeled like positive if belong to hidden language and negative otherwise. The strings of training set are correctly labeled.
I have to build a statistical classifier from training set that predicts the label of strings not seen (generalization) in the best way (better accuracy, respect to actual labeling of hidden language/automa).
I have to choose between Support Vector Machine (SVM), Recurrent Neural Network and Convolutional Neural Network.  

What could be the best choice and why?
My bet would go to a Recurrent Neural Network, as it closely models some (fuzzy, non-discrete) state machine as each character is output. A decent start to read up on RNNs for this purpose is to read the article The Unreasonable Effectiveness of Recurrent Neural Networks which describes character-level RNNs used to predict text.