| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0507131 |
| utime |
20240103222344.4 |
| mtime |
20190801235959.9 |
| SCOPUS |
84951839351 |
| WOS |
000367593500025 |
| DOI |
10.1007/978-3-319-25135-6_25 |
| title
(primary) (eng) |
Minimum Description Length Principle for Compositional Model Learning |
| specification |
| page_count |
13 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0507130 |
| ISBN |
978-3-319-25134-9 |
| ISSN |
0302-9743 |
| title
|
Integrated Uncertainty in Knowledge Modelling and Decision Making |
| part_title |
LNAI 9376 |
| page_num |
254-266 |
| publisher |
| place |
Cham |
| name |
Springer |
| year |
2015 |
|
| editor |
| name1 |
Huynh |
| name2 |
Van-Nam |
|
| editor |
| name1 |
Inuiguchi |
| name2 |
Masahiro |
|
| editor |
| name1 |
Denoeux |
| name2 |
Thierry |
|
|
| keyword |
Machine learning |
| keyword |
Multidimensional models |
| keyword |
Probability distributions |
| keyword |
Composition |
| keyword |
Information theory |
| keyword |
Lossless encoding |
| 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 |
50 |
| name1 |
Jiroušek |
| name2 |
Radim |
| institution |
UTIA-B |
| garant |
K |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0353476 |
| name1 |
Krejčová |
| name2 |
I. |
| country |
CZ |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0353428 |
| project_id |
GA15-00215S |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
Not having another source of information than source data, the process of data-based model construction can be viewed as the transformation of information represented by data into that represented by the model. The paper explains how this idea supports the Minimum Description Length Principle and how it can be employed to avoid the overfitting of the constructed model. |
| action |
| ARLID |
cav_un_auth*0377836 |
| name |
4th Intemational Symposium, IUKM 2015 |
| dates |
20151015 |
| mrcbC20-s |
20151017 |
| place |
Nha Trang |
| country |
VN |
|
| RIV |
IN |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2020 |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0298537 |
| confidential |
S |
| mrcbT16-s |
0.329 |
| mrcbT16-E |
Q2 |
| arlyear |
2015 |
| mrcbU14 |
84951839351 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000367593500025 WOS |
| mrcbU63 |
cav_un_epca*0507130 Integrated Uncertainty in Knowledge Modelling and Decision Making 978-3-319-25134-9 0302-9743 254 266 Integrated Uncertainty in Knowledge Modelling and Decision Making Cham Springer 2015 Lecture Notes in Artificial Intelligence LNAI 9376 |
| mrcbU67 |
340 Huynh Van-Nam |
| mrcbU67 |
340 Inuiguchi Masahiro |
| mrcbU67 |
340 Denoeux Thierry |
|