Experimental Demonstration of Online Learning-Based Concept Drift Adaptation for Failure Detection in Optical Networks
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912896594739200 |
|---|---|
| author | Ali, Yousuf Moiz Prilepsky, Jaroslaw E. Pedro, João Napoli, Antonio Srivallapanondh, Sasipim Turitsyn, Sergei K. Freire, Pedro |
| author_facet | Ali, Yousuf Moiz Prilepsky, Jaroslaw E. Pedro, João Napoli, Antonio Srivallapanondh, Sasipim Turitsyn, Sergei K. Freire, Pedro |
| contents | We present a novel online learning-based approach for concept drift adaptation in optical network failure detection, achieving up to a 70% improvement in performance over conventional static models while maintaining low latency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10401 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Experimental Demonstration of Online Learning-Based Concept Drift Adaptation for Failure Detection in Optical Networks Ali, Yousuf Moiz Prilepsky, Jaroslaw E. Pedro, João Napoli, Antonio Srivallapanondh, Sasipim Turitsyn, Sergei K. Freire, Pedro Machine Learning Signal Processing Optics We present a novel online learning-based approach for concept drift adaptation in optical network failure detection, achieving up to a 70% improvement in performance over conventional static models while maintaining low latency. |
| title | Experimental Demonstration of Online Learning-Based Concept Drift Adaptation for Failure Detection in Optical Networks |
| topic | Machine Learning Signal Processing Optics |
| url | https://arxiv.org/abs/2602.10401 |