| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0499667 |
| utime |
20240103221326.4 |
| mtime |
20190111235959.9 |
| SCOPUS |
85056995230 |
| WOS |
000450651000042 |
| DOI |
10.1109/MLSP.2018.8517020 |
| title
(primary) (eng) |
Dynamic Bayesian knowledge transfer between a pair of Kalman filters |
| specification |
| page_count |
6 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0499864 |
| ISBN |
978-1-5386-5478-1 |
| ISSN |
1551-2541 |
| title
|
PROCEEDINGS OF MLSP 2018 : IEEE 28th International Workshop on Machine Learning for Signal Processing |
| publisher |
| place |
Piscataway |
| name |
IEEE |
| year |
2018 |
|
|
| keyword |
Bayesian transfer learning |
| keyword |
Fully probabilistic design |
| keyword |
Kalman filtering |
| author
(primary) |
| ARLID |
cav_un_auth*0370767 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| full_dept |
Department of Adaptive Systems |
| share |
50 |
| name1 |
Papež |
| name2 |
Milan |
| institution |
UTIA-B |
| country |
CZ |
| garant |
K |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0370768 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| share |
50 |
| name1 |
Quinn |
| name2 |
Anthony |
| institution |
UTIA-B |
| country |
IE |
| garant |
S |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA18-15970S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0362986 |
|
| abstract
(eng) |
Transfer learning is a framework that includes---among other topics---the design of knowledge transfer mechanisms between Bayesian filters. Transfer learning strategies in this context typically rely on a complete stochastic dependence structure being specified between the participating learning procedures (filters). This paper proposes a method that does not require such a restrictive assumption. The solution in this incomplete modelling case is based on the fully probabilistic design of an unknown probability distribution which conditions on knowledge in the form of an externally supplied distribution. We are specifically interested in the situation where the external distribution accumulates knowledge dynamically via Kalman filtering. Simulations illustrate that the proposed algorithm outperforms alternative methods for transferring this dynamic knowledge from the external Kalman filter. |
| action |
| ARLID |
cav_un_auth*0370769 |
| name |
International Workshop on Machine Learning for Signal Processing 2018 (MLSP 2018) /28./ |
| dates |
20180917 |
| place |
Aalborg |
| country |
DK |
| mrcbC20-s |
20180920 |
|
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2019 |
| num_of_auth |
2 |
| presentation_type |
PO |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0292053 |
| mrcbC61 |
1 |
| cooperation |
| ARLID |
cav_un_auth*0345684 |
| name |
Trinity College Dublin, the University of Dublin |
| institution |
TCD |
| country |
IE |
|
| confidential |
S |
| article_num |
8517020 |
| mrcbC86 |
1 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Theory Methods|Engineering Electrical Electronic |
| arlyear |
2018 |
| mrcbU14 |
85056995230 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000450651000042 WOS |
| mrcbU63 |
cav_un_epca*0499864 PROCEEDINGS OF MLSP 2018 : IEEE 28th International Workshop on Machine Learning for Signal Processing 978-1-5386-5478-1 1551-2541 Piscataway IEEE 2018 |
|