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- W2556371018 abstract "In this paper, we propose a new dynamic learning frame- work that requires a small amount of labeled data in the beginning, then incrementally discovers informative unlabeled data to be hand-labeled and incorporates them into the training set to improve learning perfor- mance. This approach has great potential to reduce the training expense in many medical image analysis applications. The main contributions lie in a new strategy to combine confidence-rated classifiers learned on dif- ferent feature sets and a robust way to evaluate the informativeness of each unlabeled example. Our framework is applied to the problem of classifying microscopic cell images. The experimental results show that 1) our strategy is more effective than simply multiplying the predicted probabilities, 2) the error rate of high-confidence predictions is much lower than the average error rate, and 3) hand-labeling informative ex- amples with low-confidence predictions improves performance efficiently and the performance difference from hand-labeling all unlabeled data is very small." @default.
- W2556371018 created "2016-11-30" @default.
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- W2556371018 date "2005-01-01" @default.
- W2556371018 modified "2023-09-24" @default.
- W2556371018 title "Efficient Learning by Combining Confidence-Rated Classifiers to Incorporate" @default.
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