| bibtype |
J -
Journal Article
|
| ARLID |
0382596 |
| utime |
20240103201431.4 |
| mtime |
20121107235959.9 |
| WOS |
000311461700004 |
| SCOPUS |
84869095652 |
| DOI |
10.1016/j.ijar.2012.04.001 |
| title
(primary) (eng) |
Characteristic imsets for learning Bayesian network structure |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0256774 |
| ISSN |
0888-613X |
| title
|
International Journal of Approximate Reasoning |
| volume_id |
53 |
| volume |
9 (2012) |
| page_num |
1336-1349 |
| publisher |
|
|
| keyword |
learning Bayesian network structure |
| keyword |
essential graph |
| keyword |
standard imset |
| keyword |
characteristic imset |
| keyword |
LP relaxation of a polytope |
| author
(primary) |
| ARLID |
cav_un_auth*0285215 |
| name1 |
Hemmecke |
| name2 |
R. |
| country |
DE |
|
| author
|
| ARLID |
cav_un_auth*0285216 |
| name1 |
Lindner |
| name2 |
S. |
| country |
DE |
|
| author
|
| ARLID |
cav_un_auth*0101202 |
| name1 |
Studený |
| name2 |
Milan |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| institution |
UTIA-B |
| full_dept |
Department of Decision Making Theory |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
1M0572 |
| agency |
GA MŠk |
| country |
CZ |
| ARLID |
cav_un_auth*0001814 |
|
| project |
| project_id |
GA201/08/0539 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0239648 |
|
| abstract
(eng) |
In this paper we introduce a new unique vector representative, called the characteristic imset, obtained from the standard imset by an affine transformation. Characteristic imsets are (shown to be) zero-one vectors and have many elegant properties, suitable for intended application of linear/integer programming methods to learning BN structure. They are much closer to the graphical description; we describe a simple transition between the characteristic imset and the essential graph, known as a traditional unique graphical representative of the BN structure. In the end, we relate our proposal to other recent approaches which apply linear programming methods in probabilistic reasoning. |
| reportyear |
2013 |
| RIV |
BA |
| num_of_auth |
3 |
| mrcbC52 |
4 A 4a 20231122135256.4 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0212775 |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE |
| mrcbT16-f |
2.165 |
| mrcbT16-g |
0.447 |
| mrcbT16-h |
5.5 |
| mrcbT16-i |
0.00618 |
| mrcbT16-j |
0.745 |
| mrcbT16-k |
1920 |
| mrcbT16-l |
85 |
| mrcbT16-s |
1.494 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
65.146 |
| mrcbT16-C |
70.000 |
| mrcbT16-D |
Q2 |
| mrcbT16-E |
Q1 |
| arlyear |
2012 |
| mrcbTft |
\nSoubory v repozitáři: studeny-0382596.pdf |
| mrcbU14 |
84869095652 SCOPUS |
| mrcbU34 |
000311461700004 WOS |
| mrcbU63 |
cav_un_epca*0256774 International Journal of Approximate Reasoning 0888-613X 1873-4731 Roč. 53 č. 9 2012 1336 1349 Elsevier |
|