Adaptive learning refers to a type of machine learning that focuses on continuously adapting and adjusting to the input data as new data is obtained. Unlike static learning, where a machine learning model is trained once and used statically, adaptive learning allows the model to adapt and adjust as more data is collected.
In adaptive learning, the model is continuously trained with new data and uses feedback to update its parameters and adjust its behaviour. This allows the model to adapt to changing environmental conditions and improve its accuracy over time.
Adaptive learning is used in many applications, such as traffic prediction, energy demand forecasting and financial fraud detection. In these applications, the machine learning model must adapt to changing environmental conditions and continuously adjust to maintain its accuracy.
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