Online Modeling

Online modeling focuses on building and updating predictive models using continuously arriving data streams, addressing the limitations of traditional offline methods that require complete datasets beforehand. Current research emphasizes developing robust algorithms, such as those based on genetic algorithms, collaborative learning, and recurrent neural networks, to handle concept drift and resource constraints while maintaining model accuracy and interpretability. This field is crucial for applications ranging from personalized education and healthcare monitoring to real-time control of complex systems and video analysis, offering significant improvements in efficiency and adaptability compared to offline approaches.