The paper presents a family of learning algorithms that address concept drift and hidden contexts in online learning. These algorithms maintain a window of recent examples and hypotheses, store concept descriptions, and use a heuristic to monitor and adjust to changes in context. Experiments show their effectiveness under various conditions, including noise and different levels of concept drift. The FLORA framework is described, with FLORA2, FLORA3, and FLORA4 as extensions. FLORA2 dynamically adjusts the window size based on heuristic indicators. FLORA3 stores and reuses past concepts for recurring contexts. FLORA4 improves robustness to noise by using statistical confidence measures. Experiments compare these algorithms against others, showing FLORA4's effectiveness in noisy environments and its ability to adapt quickly to concept changes. The paper also discusses theoretical results on concept drift and the importance of balancing stability and robustness in learning systems.The paper presents a family of learning algorithms that address concept drift and hidden contexts in online learning. These algorithms maintain a window of recent examples and hypotheses, store concept descriptions, and use a heuristic to monitor and adjust to changes in context. Experiments show their effectiveness under various conditions, including noise and different levels of concept drift. The FLORA framework is described, with FLORA2, FLORA3, and FLORA4 as extensions. FLORA2 dynamically adjusts the window size based on heuristic indicators. FLORA3 stores and reuses past concepts for recurring contexts. FLORA4 improves robustness to noise by using statistical confidence measures. Experiments compare these algorithms against others, showing FLORA4's effectiveness in noisy environments and its ability to adapt quickly to concept changes. The paper also discusses theoretical results on concept drift and the importance of balancing stability and robustness in learning systems.