Learning under Feature Drifts in Textual Streams-Reference-Cited by-同舟云学术

Learning under Feature Drifts in Textual Streams

Published:2018-10-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 27th ACM International Conference on Information and Knowledge Management
language:
Short-container-title:

Author:

Melidis Damianos P.¹,Spiliopoulou Myra²,Ntoutsi Eirini¹

Affiliation:

1. Leibniz University Hannover, Hannover, Germany

2. Otto-von-Guericke-University Magdeburg, Magdeburg, Germany

Funder

Deutsche Forschungsgemeinschaft

European Research Council

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3269206.3271717

Reference32 articles.

1. Giulio Angiani Laura Ferrari Tomaso Fontanini Paolo Fornacciari Eleonora Iotti Federico Magliani and Stefano Manicardi. 2016. A Comparison between Preprocessing Techniques for Sentiment Analysis in Twitter.. In KDWeb . Giulio Angiani Laura Ferrari Tomaso Fontanini Paolo Fornacciari Eleonora Iotti Federico Magliani and Stefano Manicardi. 2016. A Comparison between Preprocessing Techniques for Sentiment Analysis in Twitter.. In KDWeb .

2. A survey on feature drift adaptation: Definition, benchmark, challenges and future directions

3. Learning from Time-Changing Data with Adaptive Windowing

Cited by 7 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. An incremental clustering algorithm based on semantic concepts;Knowledge and Information Systems;2024-02-15

2. Assessing Batch and Online Learning for Delivery in Full and On Time Predictions;2022 International Joint Conference on Neural Networks (IJCNN);2022-07-18

3. Concept Drift Detection for Social Media: A Survey;2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N);2021-12-17

4. Drift-Aware Multi-Memory Model for Imbalanced Data Streams;2020 IEEE International Conference on Big Data (Big Data);2020-12-10

5. Resource management for model learning at entity level;Annals of Telecommunications;2020-08-29