University of Birmingham > Talks@bham > Data Science and Computational Statistics Seminar > Recent Advances in Data Stream Learning

Recent Advances in Data Stream Learning

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If you have a question about this talk, please contact Xiaocheng Shang.

In many real-world applications, data are generated or collected continuously in the form of data streams, such as social media data analysis, spam detection and IoT systems. Data stream learning is such a machine learning area that processes data streams, trains a model and makes predictions over time. In this talk, four key research directions in data stream learning will be introduced with our recent findings and some applications, including class imbalance, concept drift, federated learning and AutoML.

This talk is part of the Data Science and Computational Statistics Seminar series.

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