Random DFA's can be approximately learned from sparse uniform examples
Kevin Lang · 1992
Approximate inference of finite state machines from sparse labeled examples has been proved NP-hard when an adversary chooses the target machine and the training set [Ang78, KV89, PW89]. We have, however, empirically found that DFA's are approximately learnable from sparse data when the target machine and training set are selected at random.