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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 Document Type V - Research Report R&D Document Type O - Ostatní Title Transferring Improved Local Kernel Design in Multi-Source Bayesian Transfer Learning, with an application in Air Pollution Monitoring in India Author(s) Nugent, Sh. (IE)
Quinn, Anthony (UTIA-B) ORCIDIssue data Praha: ÚTIA AV ČR, v. v. i.,, 2021 Series Research Report Series number 2392 Number of pages 19 s. Publication form Print - P Language eng - English Country CZ - Czech Republic Keywords fully probabilistic methods ; Bayesian Transfer Learning algorithm ; Gaussian Process ; Intrinsic Coregionalization Model ; pollution modelling Subject RIV BA - General Mathematics OECD category Applied mathematics R&D Projects GA18-15970S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 Annotation 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.Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2022
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