Learning with Marginalized Corrupted Features

@inproceedings{Maaten2013LearningWM,
  title={Learning with Marginalized Corrupted Features},
  author={Laurens van der Maaten and Minmin Chen and Stephen Tyree and Kilian Q. Weinberger},
  booktitle={ICML},
  year={2013}
}
The goal of machine learning is to develop predictors that generalize well to test data. Ideally, this is achieved by training on very large (infinite) training data sets that capture all variations in the data distribution. In the case of finite training data, an effective solution is to extend the training set with artificially created examples—which, however, is also computationally costly. We propose to corrupt training examples with noise from known distributions within the exponential… CONTINUE READING
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