5. MLPro-OA - Online Adaptivity

This framework addresses the challenge of continuously adapting to changing conditions by processing new information in real time. Unlike traditional offline approaches, which rely on predefined models trained on historical data, online-adaptive methods dynamically refine their behavior as new data becomes available. This enables continuous learning, rapid adaptation to non-stationary environments, and increased robustness in uncertain or evolving scenarios. Such methods are essential in domains requiring real-time decision-making and continuous model updates, including autonomous systems, predictive analytics, and self-optimizing processes.

The following subfields are available in MLPro-OA:

• 5.1. Online-adaptive data stream processing (OADSP)
  • 5.1.1. Overview
  • 5.1.2. Preprocessing
  • 5.1.3. Cluster analysis
  • 5.1.4. Anomaly detection
  • 5.1.5. Drift detection