Estimation of Hidden Markov Models parameters using differential evolution
Ângela Abreu Rosa de Sá, Adriano de Oliveira Andrade, Slawomir J. Nasuto, Alcimar Barbosa Soares · CentAUR (University of Reading) · 2008
Hidden Markov Models (HMMs) have been successfully applied to different modelling and classification problems from different areas over the recent years. An important step in using HMMs is the initialisation of the parameters of the model as the subsequent learning of HMM’s parameters will be dependent on these values. This initialisation should take into account the knowledge about the addressed problem and also optimisation techniques to estimate the best initial parameters given a cost function, and consequently, to estimate the best log-likelihood. This paper proposes the initialisation of Hidden Markov Models parameters using the optimisation algorithm Differential Evolution with the aim to obtain the best log-likelihood.