Online Kernel

Online kernel methods aim to efficiently learn from streaming data by adapting kernel choices and model parameters dynamically. Current research emphasizes developing algorithms with improved regret bounds (measuring performance against optimal strategies) while managing computational and memory constraints, often employing techniques like budgeted learning, multi-kernel approaches, and efficient kernel approximations (e.g., using random features or orthogonal basis). These advancements are significant for applications requiring real-time learning from large or evolving datasets, particularly in areas like online regression, classification, and reinforcement learning, where efficient and adaptive kernel selection is crucial for performance.