Stream Learning

Stream learning focuses on developing machine learning models that can efficiently and accurately process continuous data streams, adapting in real-time to evolving data distributions and concept drift. Current research emphasizes efficient data selection techniques, such as learnable prompts, and novel model architectures like those based on convolutional neural networks and transformers, often incorporating ensemble or deep learning methods for improved performance. This field is crucial for handling the ever-increasing volume of real-time data in diverse applications, including video analysis, IoT security, and astronomical data processing, enabling timely insights and improved decision-making in dynamic environments.