Selecting concise training sets from clean data

  title={Selecting concise training sets from clean data},
  author={Mark Plutowski and Halbert White},
  journal={IEEE transactions on neural networks},
  volume={4 2},
The authors derive a method for selecting exemplars for training a multilayer feedforward network architecture to estimate an unknown (deterministic) mapping from clean data, i.e., data measured either without error or with negligible error. The objective is to minimize the data requirement of learning. The authors choose a criterion for selecting training examples that works well in conjunction with the criterion used for learning, here, least squares. They proceed sequentially, selecting an… CONTINUE READING


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