Knowledge Transfer in a Pair of Uniformly Modelled Bayesian Filters
| Autor: | Lenka Pavelková, Ladislav Jirsa, Anthony Quinn |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Noise (signal processing) Bayesian probability 020206 networking & telecommunications 02 engineering and technology State (functional analysis) Minimax approximation algorithm Distribution (mathematics) Transfer (computing) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Transfer of learning Algorithm Knowledge transfer |
| Zdroj: | ICINCO (1) |
| DOI: | 10.5220/0007854104990506 |
| Popis: | The paper presents an optimal Bayesian transfer learning technique applied to a pair of linear state-space processes driven by uniform state and observation noise processes. Contrary to conventional geometric approaches to boundedness in filtering problems, a fully Bayesian solution is adopted. This provides an approximate uniform filtering distribution and associated data predictor by processing the involved bounds via a local uniform approximation. This Bayesian handling of boundedness provides the opportunity to achieve optimal Bayesian knowledge transfer between bounded-error filtering nodes. The paper reports excellent rejection of knowledge below threshold, and positive transfer above threshold. In particular, an informal variant achieves strong transfer in this latter regime, and the paper discusses the factors which may influence the strength of this transfer. |
| Databáze: | OpenAIRE |
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