| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0452709 |
| utime |
20240103211443.8 |
| mtime |
20160301235959.9 |
| title
(primary) (eng) |
Lazy Learning of Environment Model from the Past |
| specification |
| page_count |
170 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0452708 |
| ISBN |
978-80-01-05841-1 |
| title
|
SPMS 2015 |
| page_num |
1-10 |
| publisher |
| place |
Praha 2 |
| name |
Nakladatelství ČVUT- výroba, Zikova 4, Praha 6 |
| year |
2015 |
|
| editor |
|
|
| keyword |
Lazy learning |
| keyword |
local modelling |
| keyword |
prediction for optimisation |
| author
(primary) |
| ARLID |
cav_un_auth*0324503 |
| name1 |
Štěch |
| name2 |
J. |
| country |
CZ |
| share |
25 |
|
| author
|
| ARLID |
cav_un_auth*0101092 |
| name1 |
Guy |
| name2 |
Tatiana Valentine |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| share |
25 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0324504 |
| name1 |
Pálková |
| name2 |
B. |
| country |
CZ |
| share |
25 |
|
| author
|
| ARLID |
cav_un_auth*0101124 |
| name1 |
Kárný |
| name2 |
Miroslav |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| share |
25 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA13-13502S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0292725 |
|
| abstract
(eng) |
The paper addresses a lazy learning (LL) approach to decision making (DM) problem described in fully probabilistic way. The key idea of LL is to simplify the actual DM problem by using past DM problems similar to the current one. The approach can decrease computation complexity and increase quality of learning when no rich alternative information available. The proposed LL approach helps to learn the environment model based on a proximity of the past and current DM problem with Kullback-Leibler divergence serving as a proximity measure. The implemented algorithm is verified on the real data. The results show that the proposed approach improves prediction quality. |
| action |
| ARLID |
cav_un_auth*0324318 |
| name |
Stochastic and Physical Monitoring Systems (SPMS2015) |
| place |
Drhleny |
| dates |
22.06.2015-27.06.2015 |
| country |
CZ |
|
| reportyear |
2016 |
| RIV |
BB |
| num_of_auth |
4 |
| presentation_type |
ZP |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0254008 |
| cooperation |
| ARLID |
cav_un_auth*0324319 |
| institution |
Department of Mathematics FNSPE CTU in Prague |
| name |
Department of Mathematics, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague |
| country |
CZ |
|
| confidential |
S |
| arlyear |
2015 |
| mrcbU63 |
cav_un_epca*0452708 SPMS 2015 978-80-01-05841-1 1 10 SPMS 2015 Praha 2 Nakladatelství ČVUT- výroba, Zikova 4, Praha 6 2015 Stochastic and Physical Monitoring Systems |
| mrcbU67 |
Hobza Tomáš 340 |
|