Active Learning on Heterogeneous Information Networks - A Multi-armed Bandit Approach

ICDM, pp. 1350-1355, 2018.

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Abstract:

Active learning exploits inherent structures in the unlabeled data to minimize the number of labels required to train an accurate model. It enables effective machine learning in applications with high labeling cost, such as document classification and drug response prediction. We investigate active learning on heterogeneous information ne...More

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