Hierarchical Bayesian Transfer Learning Between a Pair of Kalman Filters

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Hierarchical Bayesian Transfer Learning Between a Pair of Kalman Filters

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Hierarchical Bayesian Transfer Learning Between a Pair of Kalman Filters

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

<a href="/cs/project/GA18-15970S" target="_blank" >GA18-15970S: Optimální zpracování externí stochastické znalosti vyjádřené pomocí pravděpodobnostních distribucí</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 32nd Irish Signals and Systems Conference (ISSC) 2021

ISBN

978-1-6654-3429-4

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

9467863

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Athlone

Datum konání akce

10. 6. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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