| bibtype |
J -
Journal Article
|
| ARLID |
0551618 |
| utime |
20220419073902.4 |
| mtime |
20220113235959.9 |
| SCOPUS |
85122230192 |
| DOI |
10.1016/j.knosys.2021.107879 |
| title
(primary) (eng) |
Fully probabilistic design for knowledge fusion between Bayesian filters under uniform disturbances |
| specification |
| page_count |
16 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0257173 |
| ISSN |
0950-7051 |
| title
|
Knowledge-Based System |
| volume_id |
238 |
| publisher |
|
|
| keyword |
Knowledge fusion |
| keyword |
Bayesian transfer learning |
| keyword |
Fully probabilistic design |
| keyword |
State–space models |
| keyword |
Bounded noise |
| keyword |
Bayesian inference |
| author
(primary) |
| ARLID |
cav_un_auth*0382598 |
| name1 |
Kuklišová Pavelková |
| name2 |
Lenka |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| full_dept |
Department of Adaptive Systems |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101119 |
| name1 |
Jirsa |
| name2 |
Ladislav |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0370768 |
| name1 |
Quinn |
| name2 |
Anthony |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| country |
IE |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA18-15970S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0362986 |
|
| abstract
(eng) |
This paper considers the problem of Bayesian transfer learning-based knowledge fusion between linear state-space processes driven by uniform state and observation noise processes. The target task conditions on probabilistic state predictor(s) supplied by the source filtering task(s) to improve its own state estimate. A joint model of the target and source(s) is not required and is not elicited. The resulting decision-making problem for choosing the optimal conditional target filtering distribution under incomplete modelling is solved via fully probabilistic design (FPD), i.e. via appropriate minimization of Kullback-Leibler divergence (KLD). The resulting FPD-optimal target learner is robust, in the sense that it can reject poor-quality source knowledge. In addition, the fact that this Bayesian transfer learning (BTL) scheme does not depend on a model of interaction between the source and target tasks ensures robustness to the misspecification of such a model. The latter is a problem that affects conventional transfer learning methods. The properties of the proposed BTL scheme are demonstrated via extensive simulations, and in comparison with two contemporary alternatives. |
| result_subspec |
SCOPUS |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2022 |
| num_of_auth |
3 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0326889 |
| mrcbC61 |
1 |
| cooperation |
| ARLID |
cav_un_auth*0345684 |
| name |
Trinity College Dublin, the University of Dublin |
| institution |
TCD |
| country |
IE |
|
| confidential |
S |
| article_num |
107879 |
| mrcbC91 |
C |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE |
| mrcbT16-j |
1.443 |
| mrcbT16-s |
2.065 |
| mrcbT16-D |
Q2 |
| mrcbT16-E |
Q1 |
| arlyear |
2022 |
| mrcbU14 |
85122230192 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0257173 Knowledge-Based System 0950-7051 1872-7409 Roč. 238 č. 1 2022 Elsevier |
|