Master of Science (MS)
Fabio Di Troia
momemtum, Hidden Markov Models
Momentum is a popular technique for improving convergence rates during gradient descent. In this research, we experiment with adding momentum to the Baum-Welch expectation-maximization algorithm for training Hidden Markov Models. We compare discrete Hidden Markov Models trained with and without momentum on English text and malware opcode data. The effectiveness of momentum is determined by measuring the changes in model score and classification accuracy due to momentum. Experiments indicate that adding momentum to Baum-Welch can reduce the number of iterations required for initial convergence during HMM training, particularly in cases where the model is slow to converge. However, momentum does not seem to improve final model performance at higher numbers of iterations.
Miller, Andrew, "Hidden Markov Models with Momentum" (2022). Master's Projects. 1085.
Available for download on Saturday, May 27, 2023