Unsupervised Unlearning of Concept Drift with Autoencoders-Reference-Cited by-同舟云学术

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Unsupervised Unlearning of Concept Drift with Autoencoders

Published:2023-12-05 Issue: Volume:565 Page:703-710
ISSN:
Container-title:2023 IEEE Symposium Series on Computational Intelligence (SSCI)
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Author:

Artelt André¹^ORCID,Malialis Kleanthis²^ORCID,Panayiotou Christos G.²^ORCID,Polycarpou Marios M.²^ORCID,Hammer Barbara¹^ORCID

Affiliation:

1. Bielefeld University,Faculty of Technology,Bielefeld,Germany

2. KIOS Research and Innovation Center of Excellence

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10371778/10371788/10372001.pdf?arnumber=10372001

Reference35 articles.

1. Learning in Nonstationary Environments: A Survey

2. A survey on concept drift adaptation

3. Incremental on-line learning: A review and comparison of state of the art algorithms

4. Learning in the presence of concept drift and hidden contexts

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Unsupervised Incremental Learning with Dual Concept Drift Detection for Identifying Anomalous Sequences;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

3. Enhancing Drift Detection and Model Uncertainty Handling in Imbalanced Streaming Data using Autoencoder-based Approach;2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon);2023-08-18

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