| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0497538 |
| utime |
20240111141011.4 |
| mtime |
20181204235959.9 |
| title
(primary) (eng) |
Compositional Models for Data Mining: an Example |
| specification |
| page_count |
12 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 |
90-101 |
| publisher |
| place |
Japan |
| name |
Aoyama Gakuin University, Japan |
| year |
2018 |
|
| editor |
| name1 |
Sung |
| name2 |
Shao-Chin |
|
| editor |
|
|
| keyword |
compositional model |
| keyword |
data mining |
| keyword |
conditional independence |
| keyword |
mutual information |
| author
(primary) |
| ARLID |
cav_un_auth*0101118 |
| 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 |
Jiroušek |
| name2 |
Radim |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0216188 |
| 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 |
Kratochvíl |
| name2 |
Václav |
| institution |
UTIA-B |
| country |
CZ |
| 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 |
GA AV ČR |
| country |
CZ |
|
| abstract
(eng) |
Like Bayesian networks, compositional models may also be used for data mining. Nevertheless, one can find several reasons why to prefer compositional models for this purpose. Perhaps the most important is the fact that compositional models are assembled from low-dimensional (unconditional) distributions so that computationally advantageous formulas are known for information theoretic characteristics computation. The other reason is that a decomposition is a natural way of complex tasks simplification. Therefore, the inverse process of composition is easily understandable for specialists from many fields of applications regardless of their level of mathematical education. |
| action |
| ARLID |
cav_un_auth*0368378 |
| name |
The 21st Czech-Japan Seminar on Data Analysis and Decision Making |
| dates |
20181123 |
| mrcbC20-s |
20181126 |
| place |
Kamakura |
| country |
JP |
|
| 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/11104/0291221 |
| 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 90 101 978-80-7464-932-5 |
| mrcbU67 |
340 Sung Shao-Chin |
| mrcbU67 |
340 Vlach Milan |
|