0551618 20250313101606.320220113235959.9 85122230192 000779159800015 10.1016/j.knosys.2021.107879 16 s. P cav_un_epca*02571730950-7051238Elsevier Knowledge fusion Bayesian transfer learning Fully probabilistic design State–space models Bounded noise Bayesian inference cav_un_auth*0382598 Kuklišová Pavelková Lenka UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101119 Jirsa Ladislav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0370768 Quinn Anthony UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems IE Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2022/AS/kuklisova-0551618.pdf https://www.sciencedirect.com/science/article/pii/S0950705121010388 GA18-15970S GA ČR CZ cav_un_auth*0362986 This paper considers the problem of Bayesian transfer learning-based knowledge fusion between linear state-space processes driven by uniform state and observation noise processes. The target task conditions on probabilistic state predictor(s) supplied by the source filtering task(s) to improve its own state estimate. A joint model of the target and source(s) is not required and is not elicited. The resulting decision-making problem for choosing the optimal conditional target filtering distribution under incomplete modelling is solved via fully probabilistic design (FPD), i.e. via appropriate minimization of Kullback-Leibler divergence (KLD). The resulting FPD-optimal target learner is robust, in the sense that it can reject poor-quality source knowledge. In addition, the fact that this Bayesian transfer learning (BTL) scheme does not depend on a model of interaction between the source and target tasks ensures robustness to the misspecification of such a model. The latter is a problem that affects conventional transfer learning methods. The properties of the proposed BTL scheme are demonstrated via extensive simulations, and in comparison with two contemporary alternatives. SCOPUS BB 10000 10100 10103 2022 3 2 4 R hod 4 4rh 4 20250310142053.1 20250310142459.5 RVO:67985556 http://hdl.handle.net/11104/0326889 1 cav_un_auth*0345684 Trinity College Dublin, the University of Dublin TCD IE S 107879 C COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE 8.6 1.7 3.4 0.03615 1.443 36687 2.065 7.700 1246 Q1 87.2 Q2 Q1 1.5 Q1 87.2 2022 Soubory v repozitáři: kuklisova-551618.pdf 85122230192 SCOPUS PUBMED 000779159800015 WOS cav_un_epca*0257173 Knowledge-Based System Roč. 238 č. 1 2022 0950-7051 1872-7409 Elsevier