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 |
|
|
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 |
|
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 |
|