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Description
  • Není k dispozici (cs)
  • The paper presents new application of Particle Swarm Optimization for training Hidden Markov Models. The approach is verified on artificial data and further, the application to Intracranial Pressure (ICP) analysis is described. In comparison with Expectation Maximization algorithm, commonly used for the HMM training problem, the PSO approach is less sensitive on sticking to local optima because of its global character. However this advantage depends on character of the particular problem. The IC analysis is the case of such problem where it is suitable to use the PSO strategy. This is demonstrated by better classification result (85.1%) in comparison with the EM algorithm (76.3%).
  • The paper presents new application of Particle Swarm Optimization for training Hidden Markov Models. The approach is verified on artificial data and further, the application to Intracranial Pressure (ICP) analysis is described. In comparison with Expectation Maximization algorithm, commonly used for the HMM training problem, the PSO approach is less sensitive on sticking to local optima because of its global character. However this advantage depends on character of the particular problem. The IC analysis is the case of such problem where it is suitable to use the PSO strategy. This is demonstrated by better classification result (85.1%) in comparison with the EM algorithm (76.3%). (en)
Title
  • Particle Swarm Optimization for Hidden Markov Models with Application to Intracranial Pressure Analysis
  • Není k dispozici (cs)
  • Particle Swarm Optimization for Hidden Markov Models with Application to Intracranial Pressure Analysis (en)
skos:prefLabel
  • Particle Swarm Optimization for Hidden Markov Models with Application to Intracranial Pressure Analysis
  • Není k dispozici (cs)
  • Particle Swarm Optimization for Hidden Markov Models with Application to Intracranial Pressure Analysis (en)
skos:notation
  • RIV/68407700:21230/06:03120561!RIV07-MSM-21230___
http://linked.open.../vavai/riv/strany
  • 175 ; 177
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6840770012)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 491572
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/06:03120561
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Evolution; Expectation Maximization; Hidden Markov Models; Intracranial pressure; Nature-inspired; Particle Swarm Optimization (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [33FD89920AE1]
http://linked.open...v/mistoKonaniAkce
  • Brno
http://linked.open...i/riv/mistoVydani
  • Brno
http://linked.open...i/riv/nazevZdroje
  • Analysis of Biomedical Signals and Images - Proceedings of Biosignal 2006
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Lhotská, Lenka
  • Novák, Daniel
  • Macaš, Martin
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • VUTIUM Press
https://schema.org/isbn
  • 80-214-3152-0
http://localhost/t...ganizacniJednotka
  • 21230
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