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Active Learning
- Class labels are expensive to obtain
- Active learner: query human (oracle) for labels
- Pool-based approach: Uses a pool of unlabeled data
- L: a small subset of D is labeled, U: a pool of unlabeled data in D
- Use a query function to carefully select one or more tuples from U and request labels from an oracle (a human annotator)
- The newly labeled samples are added to L, and learn a model
- Goal: Achieve high accuracy using as few labeled data as possible
- Evaluated using learning curves: Accuracy as a function of the number of instances queried (# of tuples to be queried should be small)
- Research issue: How to choose the data tuples to be queried?
- Uncertainty sampling: choose the least certain ones
- Reduce version space, the subset of hypotheses consistent w. the training data
- Reduce expected entropy over U: Find the greatest reduction in the total number of incorrect predictions
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