Bayesian Online Learning for MEC Object Recognition Systems
| Autor: | Jose A. Ayala-Romero, Apostolos Galanopoulos, George Iosifidis, Douglas J. Leith |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science 020208 electrical & electronic engineering Bayesian probability Cognitive neuroscience of visual object recognition Inference 020206 networking & telecommunications Regret 02 engineering and technology Machine learning computer.software_genre symbols.namesake 0202 electrical engineering electronic engineering information engineering symbols Key (cryptography) Leverage (statistics) Augmented reality Artificial intelligence Online algorithm business Gaussian process computer |
| Zdroj: | GLOBECOM |
| DOI: | 10.1109/globecom42002.2020.9322146 |
| Popis: | Real-time object recognition is becoming an essen-tial part of many emerging services, such as augmented reality, which require accurate inference in a timely fashion with low delay. We consider an edge-assisted object recognition system that can be configured in ways that have diverse impacts on these key performance criteria. Our goal is to design an online algorithm that learns the optimal configuration of the system by observing the outcomes of configurations applied in the past. We leverage the structure of the problem and combine a Gaussian process with a multi-armed bandit framework to efficiently solve the problem at hand. Our results indicate that our solution makes better configuration choices compared to other bandit algorithms, resulting in lower regret. |
| Databáze: | OpenAIRE |
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