Practical Algorithms for On-line Sampling

@inproceedings{Domingo1998PracticalAF,
  title={Practical Algorithms for On-line Sampling},
  author={Carlos Domingo and Ricard Gavald{\`a} and Osamu Watanabe},
  booktitle={Discovery Science},
  year={1998}
}
One of the core applications of machine learning to knowledge discovery is building a hypothesis (such as a decision tree or neural network) from a given amount of data, so that we can later use it to predict new instances of the data. In this paper, we focus on a particular situation where we assume that the hypothesis we want to use for prediction is a very simple one so the hypotheses class is of feasible size. We study the problem of how to determine which of the hypotheses in the class is… Expand
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