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Transferring Improved Local Kernel Design in Multi-Source Bayesian Transfer Learning, with an application in Air Pollution Monitoring in India
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SYSNO ASEP 0550881 Druh ASEP V - Výzkumná zpráva Zařazení RIV O - Ostatní Název Transferring Improved Local Kernel Design in Multi-Source Bayesian Transfer Learning, with an application in Air Pollution Monitoring in India Tvůrce(i) Nugent, Sh. (IE)
Quinn, Anthony (UTIA-B) ORCIDVyd. údaje Praha: ÚTIA AV ČR, v. v. i.,, 2021 Edice Research Report Č. sv. edice 2392 Poč.str. 19 s. Forma vydání Tištěná - P Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika Klíč. slova fully probabilistic methods ; Bayesian Transfer Learning algorithm ; Gaussian Process ; Intrinsic Coregionalization Model ; pollution modelling Vědní obor RIV BA - Obecná matematika Obor OECD Applied mathematics CEP GA18-15970S GA ČR - Grantová agentura ČR Institucionální podpora UTIA-B - RVO:67985556 Anotace Existing frameworks for multi-task learning [1],[2] often rely on completely modelled relationships between tasks, which may not be available. Recent work [3], [4] has been undertaken on approaches to fully probabilistic methods for transfer learning between two Gaussian Process (GP) tasks. There, the target algorithm accepts source knowledge in the form of a probabilistic prior from a source algorithm, without requiring the target to model their interaction with the source. These strategies have offered robust improvements on current state of the art algorithms, such as the Intrinsic Coregionalization Model. The Bayesian Transfer Learning algorithm proposed in [4], was found to provide robust, positive
transfer. This algorithm was then extended to accommodate knowledge transfer from multiple source modellers [5]. Improved predictive performance was observed from increases in the number of sources. This report reviews the multi-source transfer findings in [5] and applies it to a real world problem of pollution modelling in India, using public-domain data.Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2022
Počet záznamů: 1