Жевлакова К. Е.

The article explores modern approaches to adaptive machine learning in real-time environments, focusing on the problem of concept drift in streaming data. It examines the main methods for detecting and correcting data drift, which allow models to maintain accuracy when working with non-stationary information streams. The article analyzes online learning algorithms, adaptive threshold mechanisms, and incremental model update techniques. It also discusses the potential applications of these methods in monitoring, forecasting, and automation systems, as well as the challenges associated with comp...

The article explores modern approaches to adaptive machine learning in real-time environments, focusing on the problem of concept drift in streaming data. It examines the main methods for detecting and correcting data drift, which allow models to maintain accuracy when working with non-stationary information streams. The article analyzes online learning algorithms, adaptive threshold mechanisms, and incremental model update techniques. It also discusses the potential applications of these methods in monitoring, forecasting, and automation systems, as well as the challenges associated with comp...