Online Machine Learning

Online machine learning (OML) focuses on developing algorithms that can learn and adapt continuously from sequentially arriving data streams, unlike batch methods that require complete datasets upfront. Current research emphasizes efficient algorithms for handling non-stationary data, including those with concept drift and anomalies, often employing techniques like incremental learning and adaptive model updates. OML's ability to process large, evolving datasets in real-time is driving its adoption in diverse applications, such as anomaly detection in time series, 3D reconstruction from X-ray data, and self-adaptive systems in smart cities and manufacturing, improving efficiency and enabling real-time decision-making. Standardized evaluation methods are also emerging to ensure reliable comparisons and facilitate broader adoption.