bibtype L4 - Prototype, methodology, f. module, software
ARLID 0484197
utime 20240103215306.9
mtime 20180108235959.9
title (primary) (eng) Clustering and Classification Using Recursive Mixture Estimation. Software package
publisher
pub_time 2018
keyword software
keyword mixture-based clustering
keyword recursive mixture estimation
author (primary)
ARLID cav_un_auth*0101167
name1 Nagy
name2 Ivan
full_dept (cz) Zpracování signálů
full_dept (eng) Department of Signal Processing
department (cz) ZS
department (eng) ZS
institution UTIA-B
full_dept Department of Signal Processing
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0355927
name1 Suzdaleva
name2 Evženie
full_dept (cz) Zpracování signálů
full_dept Department of Signal Processing
department (cz) ZS
department ZS
institution UTIA-B
full_dept Department of Signal Processing
country RU
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0205791
name1 Pecherková
name2 Pavla
full_dept (cz) Zpracování signálů
full_dept Department of Signal Processing
department (cz) ZS
department ZS
institution UTIA-B
full_dept Department of Signal Processing
country CZ
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2017/ZS/suzdaleva-0484197.pdf
source
url http://library.utia.cas.cz/separaty/2017/ZS/suzdaleva-0484197.zip
cas_special
project
ARLID cav_un_auth*0321440
project_id GA15-03564S
agency GA ČR
abstract (eng) The software package includes programs, which were developed within the project GA ČR GA15-03564S „Clustering and Classification Using Recursive Mixture Estimation“. The main contribution of the project was to propose and implement the algorithms for mixture-based clustering and classification for various combinations of components as well as pointer models in the programming free and open source environment Scilab (www.scilab.org).
RIV BB
FORD0 10000
FORD1 10100
FORD2 10103
reportyear 2018
num_of_auth 3
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0279549
confidential S
arlyear 2018
mrcbU10 2018