Online Transfer Learning for RSV Case Detection

In such cases, transferring knowledge from the source domain becomes crucial, particularly because the Machine learning has made substantial advancements in limited initial data in the target domain may be insufficient recent decades, with its applications spanning a wide range of for effective learning. The extensive and diverse information fields such as image and speech recognition, natural language available from the source domains can significantly compensate processing, and autonomous driving. Despite these achievements, for this shortfall, providing a foundational knowledge base machine learning in biomedicine faces significant challenges, that the model can build upon as more target domain data particularly in data collection. The acquisition of labeled becomes available. Therefore, the efficiency and effectiveness data can be very costly or even unfeasible due to factors of learning in the target domain are greatly enhanced by the like ethical considerations, patient privacy, and the scarcity transferred knowledge from the source domains. of certain diseases. These challenges have led researchers to Online transfer learning entails leveraging knowledge from increasingly rely on utilizing data from related domains that a static source domain and applying it to an ongoing, evolving have a more abundant supply of data.