Adaptive Function

Adaptive function research explores how systems dynamically adjust their parameters or behavior to optimize performance in changing environments. Current work focuses on developing algorithms and models, such as adaptive step-size methods in graph matching and customized activation functions in neural networks, that enhance efficiency, accuracy, and robustness. This research is significant for improving machine learning models, particularly in areas like image analysis and data analytics, by enabling more efficient learning and better generalization to unseen data. Applications range from optimizing IoT healthcare systems to enhancing target tracking algorithms.