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A ml beginner here, so please bear with me. If I understand correctly RNNs seem to be the go to method right now for sequence prediction for a given input (single/as a sequence). But I do not have sufficient data to train a RNN. I have discounted Markov decision process based mechanisms for the same reason.

Are there any online learning algos that I can use to get coarse/approximate predicted sequences with only a small training set? I have looked at Q-learning but it seems to be ideal for best path problems where the end goal is definite.

Any pointers would be greatly appreciated.

UPDATE: Adding more clarity on the type of data post the comments. My data is video content consumption data. About 100 users consuming from a library of 1000 video titles. Intent is to exploit (if it exists) the likelihood of consuming content in a probable sequence. I currently have 300 - 400 such sequences spanning 3 videos each.

Welcome to DataScience.SE! The traditional way to deal with limited data is to introduce assumptions through Bayesian priors, or stronger regularization. What do you know about your data generation process? – Emre – 2016-06-20T18:36:02.417

Could you tell me how small is your training set? Basically if you could provide more details on regarding your problem would be nice. – ahajib – 2016-06-20T20:22:04.487