Processing hidden Markov models using recurrent neural networks for biological applications
Pavan Kumar Rallabandi · University of the Western Cape Electronic Theses and Dissertations Repository (University of the Western Cape) · 2013
In this thesis, we present a novel hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov Models (HMMs).Though sequence recognition problems could be potentially modelled through well trained HMMs, they could not provide a reasonable solution to the complicated recognition problems.In contrast, the ability of RNNs to recognize the complex sequence recognition problems is known to be exceptionally good.It should be noted that in the past, methods for applying HMMs into RNNs have been developed by DEDICATION I dedicate this thesis to my parents, grand parents, and GOD.