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In this paper, a new active learning scheme is proposed for linear regression problems with the objective of resolving the insufficient training data problem and the unreliable training data labeling problem. A pool-based active regression technique is applied to select the optimal training data to label from the overall data pool. Then, compressive sensing is exploited to remove labeling errors if...
In this letter, we develop an active learning algorithm to optimize the selection of training data for robust linear regression. This algorithm selects training data based on the principle of correlation matching between the training dataset and the overall data pool. Considering the inevitable and potentially heavy human labeling errors, we model the probability of labeling errors based on the item...
In this paper, we propose a joint machine learning and human learning design approach to make the training data labeling task in linear regression problems more efficient and robust to noise, modeling mismatch, and human labeling errors. Considering a sequential active learning scheme which relies on human learning to enlarge training data set, we integrate it with sparse outlier detection algorithms...
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