# How can active learning be used in the case of complex models that require a lot of data?

We have a series of data and we want to label the parts of each series. As we do not have any training data, we could try to use active learning as a solution, but the problem is that our classifier is something like RNN which needs a lot of data to be trained. Hence, we have a problem in converging fast to just label proportional small parts of unlabeled data.

As I found this case backs to the sequence labeling. Sequence labeling has some classic solution such as conditional random fields (CRFs) and hidden Markov model (HMM). Also, have some solution in Active learning (AL) which use from algorihtms such as struct SVM ($$\text{SVM}^{\text{strcut}}$$) like this paper.