Hierarchical Bayesian Transfer Learning Between a Pair of Kalman Filters
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00549007" target="_blank" >RIV/67985556:_____/21:00549007 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ISSC52156.2021.9467863" target="_blank" >http://dx.doi.org/10.1109/ISSC52156.2021.9467863</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ISSC52156.2021.9467863" target="_blank" >10.1109/ISSC52156.2021.9467863</a>
Alternative languages
Result language
angličtina
Original language name
Hierarchical Bayesian Transfer Learning Between a Pair of Kalman Filters
Original language description
Transfer learning strategies are typically designed in a deterministic manner, without processing uncertainty in the knowledge transfer mechanism. They also require the dependence between the participating learning procedures-Bayesian filters in this work-to be explicitly modelled. This letter develops an approach which relaxes both of these restrictive assumptions. We frame the proposed Bayesian transfer learning technique as fully probabilistic design of an unknown hierarchical probability distribution conditioned on knowledge in the form of an external probability distribution. This yields a randomized design around a base density for transfer learning which has been reported in previous work by the authors. In the Kalman filtering context, this hierarchical relaxation-which induces a knowledge-driven mixture state predictor-significantly improves tracking performance when compared to conventional transfer learning methods.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
<a href="/en/project/GA18-15970S" target="_blank" >GA18-15970S: Optimal Distributional Design for External Stochastic Knowledge Processing</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 32nd Irish Signals and Systems Conference (ISSC) 2021
ISBN
978-1-6654-3429-4
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
9467863
Publisher name
IEEE
Place of publication
Piscataway
Event location
Athlone
Event date
Jun 10, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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