Particle Swarm Optimization of Hidden Markov Models: a comparative study

In recent years, Hidden Markov Models (HMM) have been increasingly applied in data mining applications. However, most authors have used classical optimization ExpectationMaximization (EM) scheme. A new method of HMM learning based on Particle Swarm Optimization (PSO) has been developed. Along with others global approaches as Simulating Annealing (SIM) and Genetic Algorithms (GA) the following local gradient methods have been also compared: classical Expectation-Maximization algorithm, Maximum A Posteriory approach (MAP) and Bayes Variational learning (VAR). The methods are evaluated on a synthetic data set using different evaluation criteria including classification problem. The most reliable optimization approach in terms of performance, numerical stability and speed is VAR learning followed by PSO approach.