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Active learning is well-motivated in many supervised learning tasks where unlabeled data may be abundant but labeled examples are expensive to obtain. The goal of active learning is to maximize the performance of a learning model using as few labeled training data as possible, thereby minimizing the cost of data annotation. So far, there is still very limited work on active learning for regression...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.