bibtype C - Conference Paper (international conference)
ARLID 0497540
utime 20240111141011.4
mtime 20181204235959.9
title (primary) (eng) Efficient implementation of compositional models for data mining
specification
page_count 8 s.
media_type P
serial
ARLID cav_un_epca*0497537
ISBN 978-80-7464-932-5
title Proceedings of the 21st Czech-Japan Seminar od Data Analysis and Decision Making
page_num 80-87
publisher
place Japan
name Aoyama Gakuin University, Japan
year 2018
editor
name1 Sung
name2 Shao-Chin
editor
name1 Vlach
name2 Milan
keyword data mining
keyword mutual information
keyword compositional models
keyword conditional independence
keyword probability theory
author (primary)
ARLID cav_un_auth*0216188
full_dept (cz) Matematická teorie rozhodování
full_dept (eng) Department of Decision Making Theory
department (cz) MTR
department (eng) MTR
full_dept Department of Decision Making Theory
share 34
name1 Kratochvíl
name2 Václav
institution UTIA-B
country CZ
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101118
full_dept (cz) Matematická teorie rozhodování
full_dept Department of Decision Making Theory
department (cz) MTR
department MTR
full_dept Department of Decision Making Theory
share 33
name1 Jiroušek
name2 Radim
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0368377
share 33
name1 Lee
name2 T. R.
country TW
source
source_type PDF
url http://library.utia.cas.cz/separaty/2018/MTR/kratochvil-0497540.pdf
cas_special
project
ARLID cav_un_auth*0356801
project_id MOST-18-04
agency AV ČR
country CZ
country TW
project
project_id GA16-12010S
agency GA ČR
country CZ
ARLID cav_un_auth*0332303
abstract (eng) A compositional model encodes probabilistic relationships among variables of interest. In connection with various statistical techniques, it represents a practical tool for data modeling and data mining. Structure of the model represents (un)conditional independencies among all variables. Relationships of dependent variables are described by low-dimensional probability distributions. Having a compositional model, a data miner can easily apply an intervention on variables of interest, fix values of other variables (conditioning), or to narrow the context of a problem (marginalization). The model learning process can be controlled to avoid overfitting of data.\n\nIn this paper, we present a new semi-supervised web application that will enable researchers to design probabilistic (compositional) models (both causal and stochastic). Thanks to the web architecture of the system, the researchers will always have a possibility to influence the data-based model construction process from any place of the world. It is also expected that the application of this methodology to practical problems will open new problems that will be an inspiration for further theoretical research.
action
ARLID cav_un_auth*0368378
name The 21st Czech-Japan Seminar on Data Analysis and Decision Making
dates 20181123
place Kamakura
country JP
mrcbC20-s 20181126
RIV IN
FORD0 20000
FORD1 20200
FORD2 20205
reportyear 2019
num_of_auth 3
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0291220
cooperation
ARLID cav_un_auth*0368382
name National Chung Hsing University
country TW
confidential S
arlyear 2018
mrcbU14 SCOPUS
mrcbU24 PUBMED
mrcbU34 WOS
mrcbU56 PDF
mrcbU63 cav_un_epca*0497537 Proceedings of the 21st Czech-Japan Seminar od Data Analysis and Decision Making Aoyama Gakuin University, Japan 2018 Japan 80 87 978-80-7464-932-5
mrcbU67 340 Sung Shao-Chin
mrcbU67 340 Vlach Milan