bibtype |
V -
Research Report
|
ARLID |
0377918 |
utime |
20240103201011.3 |
mtime |
20120828235959.9 |
title
(primary) (eng) |
LP relaxations and pruning for characteristic imsets |
publisher |
place |
Praha |
name |
ÚTIA AVČR |
pub_time |
2012 |
|
specification |
|
edition |
name |
Research Report |
volume_id |
2323 |
|
keyword |
learning Bayesian network structure |
keyword |
quality criterion |
keyword |
integer linear programming |
author
(primary) |
ARLID |
cav_un_auth*0101202 |
name1 |
Studený |
name2 |
Milan |
full_dept (cz) |
Matematická teorie rozhodování |
full_dept (eng) |
Department of Decision Making Theory |
department (cz) |
MTR |
department (eng) |
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 |
GA201/08/0539 |
agency |
GA ČR |
ARLID |
cav_un_auth*0239648 |
|
abstract
(eng) |
The geometric approach to learning BN structure is to represent it by a certain vector; a suitable such zero-one vector is the characteristic imset, which allows to reformulate the task of finding global maximum of a score over BN structures as an integer linear programming problem. The main contribution of this report is an LP relaxation of the corresponding polytope, that is, a polyhedral description of the domain of the respective integer linear programming problem. |
reportyear |
2013 |
RIV |
BA |
num_of_auth |
1 |
mrcbC52 |
4 O 4o 20231122135116.2 |
inst_support |
RVO:67985556 |
permalink |
http://hdl.handle.net/11104/0209940 |
arlyear |
2012 |
mrcbTft |
\nSoubory v repozitáři: 0377918.pdf |
mrcbU10 |
2012 |
mrcbU10 |
Praha ÚTIA AVČR |
|