| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0517961 |
| utime |
20241106135801.7 |
| mtime |
20191216235959.9 |
| SCOPUS |
85081363071 |
| WOS |
000568621300052 |
| DOI |
10.1109/ISSPIT47144.2019.9001885 |
| title
(primary) (eng) |
Bayesian transfer learning between Gaussian process regression tasks |
| specification |
| page_count |
6 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0519792 |
| ISBN |
978-1-7281-5341-4 |
| title
|
Proceedings of the IEEE International Symposium on Signal Processing and Information Technology 2019 (ISSPIT 2019) |
| publisher |
| place |
Piscataway |
| name |
IEEE |
| year |
2019 |
|
|
| keyword |
Bayesian transfer learning |
| keyword |
supervised learning |
| keyword |
fully probabilistic design |
| keyword |
incomplete modelling |
| keyword |
Gaussian process regression |
| author
(primary) |
| ARLID |
cav_un_auth*0370767 |
| name1 |
Papež |
| name2 |
Milan |
| 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*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 |
|
| cas_special |
| project |
| project_id |
GA18-15970S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0362986 |
|
| abstract
(eng) |
Bayesian knowledge transfer in supervised learning scenarios often relies on a complete specification and optimization of the stochastic dependence between source and target tasks. This is a critical requirement of completely modelled settings, which can often be difficult to justify. We propose a strategy to overcome this. The methodology relies on fully probabilistic design to develop a target algorithm which accepts source knowledge in the form of a probability distribution. We present this incompletely modelled setting in the supervised learning context where the source and target tasks are to perform Gaussian process regression. Experimental evaluation demonstrates that the transfer of the source distribution substantially improves prediction performance of the target learner when recovering a distorted nonparametric function realization from noisy data. |
| action |
| ARLID |
cav_un_auth*0385149 |
| name |
IEEE International Symposium on Signal Processing and Information Technology 2019 (ISSPIT 2019) /19./ |
| dates |
20191209 |
| mrcbC20-s |
20191212 |
| place |
Ajman |
| country |
AE |
|
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2021 |
| mrcbC52 |
4 A sml 4as 20241106135801.7 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0303180 |
| mrcbC61 |
1 |
| confidential |
S |
| contract |
| name |
IEEE COPYRIGHT AND CONSENT FORM |
| date |
20191114 |
|
| mrcbC86 |
n.a. Proceedings Paper Computer Science Hardware Architecture|Engineering Electrical Electronic|Telecommunications |
| arlyear |
2019 |
| mrcbTft |
\nSoubory v repozitáři: papez-0517961-copyright_receipt.pdf |
| mrcbU14 |
85081363071 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000568621300052 WOS |
| mrcbU63 |
cav_un_epca*0519792 Proceedings of the IEEE International Symposium on Signal Processing and Information Technology 2019 (ISSPIT 2019) 978-1-7281-5341-4 Piscataway IEEE 2019 |
|