Particle Swarm Optimization of Hidden Markov Models: a comparative study

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A03145659" target="_blank" >RIV/68407700:21230/08:03145659 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Particle Swarm Optimization of Hidden Markov Models: a comparative study
Original language description
In recent years, Hidden Markov Models (HMM) have been increasingly applied in data mining applications. However, most authors have used classical optimization Expectation- Maximization (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.
Czech name
Optimalizace markovských modelů pomocí SWO
Czech description
Článek popisuje optimalizaci markovských modelů pomocí techniky SWO

Publication year
2008
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)