Adaptive teaching: Improving the efficiency of learning through hypothesis-dependent selection of training data

@inproceedings{Chan2014AdaptiveTI,
  title={Adaptive teaching: Improving the efficiency of learning through hypothesis-dependent selection of training data},
  author={Patricia Angie Chan and Douglas Markant and Brenden M. Lake and Todd M. Gureckis},
  booktitle={CogSci},
  year={2014}
}
Active machine learning research shows that training of classifiers can be improved when the learning algorithm itself selects training data (e.g., choosing examples for which it is uncertain). Recent work with humans documents similar improvements whereby ”active” learners who can select their own training examples are faster at learning simple… CONTINUE READING