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