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Sequence Prediction – Hidden Markov Model and Recurrent Neural Networks

B. Ganapathy Subramaniam, Dr. T. Rama Prabha, Daniel Steven


Machine learning is a hot topic nowadays and one of its subtypes is sequence prediction. Sequence prediction is the process of predicting an event or a series of events based on past observations. This can be done using some models developed specifically for sequence learning. Two of the more popular and prevalent models are Hidden Markov Model and Recurrent Neural Networks. Hidden Markov Models are primarily a probabilistic model. They perform well in scenarios where data over time steps do not need to be retained, but have an inherent disadvantage in sequences that need its data and order to be retained. Recurrent Neural Networks solve that problem using several methods, and one of those is Long Short-Term Memory. Long Short-Term Memory keeps track of all the data that came before in an efficient manner. We have a brief overview of these two models and see how the two compares with each other.


Sequence Prediction, Hidden Markov Model, Recurrent Neural Network, Long Short-Term Memory, Part-Of-Speech Tagging, Machine Learning, Natural Language Processing

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