bibtype C - Conference Paper (international conference)
ARLID 0410563
utime 20240103182222.7
mtime 20060210235959.9
ISBN 3-211-83651-9
title (primary) (eng) Number of components and initialization in Gaussian mixture model for pattern recognition
publisher
place Wien
name Springer
pub_time 2001
specification
page_count 4 s.
serial
title Artificial Neural Nets and Genetic Algorithms. Proceedings
page_num 406-409
editor
name1 Kůrková
name2 J.
editor
name1 Neruda
name2 R.
editor
name1 Kárný
name2 M.
editor
name1 Steele
name2 N. C.
keyword pattern recognition
keyword Gaussian mixture model
keyword kernel density estimate
author (primary)
ARLID cav_un_auth*0212668
name1 Paclík
name2 P.
country NL
author
ARLID cav_un_auth*0101171
name1 Novovičová
name2 Jana
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
COSATI 12B
COSATI 09K
cas_special
project
project_id VS96063
agency GA MŠk
ARLID cav_un_auth*0025066
project
project_id KSK1075601
agency GA AV ČR
ARLID cav_un_auth*0027435
research AV0Z1075907
abstract (eng) The method for complete mixture initialization based on a product kernel estimate of probability density function is proposed for mixture estimation using EM-algorithm. The mixture components are assumed to correspond to local maxima of optimaly smoothed kernel density estimate. The gradient method is used for local extrema finding. As the last step, agglomerative hiearchical clustering methods merges closest components together. A comparison to scale-space approaches is given on examples.
action
ARLID cav_un_auth*0212760
name International Conference on Artificial Neural Nets and Genetic Algorithms /5./
place Prague
country CZ
dates 22.04.2001-25.04.2001
RIV BB
department RO
permalink http://hdl.handle.net/11104/0130652
ID_orig UTIA-B 20010032
arlyear 2001
mrcbU10 2001
mrcbU10 Wien Springer
mrcbU12 3-211-83651-9
mrcbU63 Artificial Neural Nets and Genetic Algorithms. Proceedings 406 409
mrcbU67 Kůrková J. 340
mrcbU67 Neruda R. 340
mrcbU67 Kárný M. 340
mrcbU67 Steele N. C. 340