Autmatic Parameter Selection by Minimizing Estimated Error

@inproceedings{Kohavi1995AutmaticPS,
  title={Autmatic Parameter Selection by Minimizing Estimated Error},
  author={Ron Kohavi and George H. John},
  booktitle={ICML},
  year={1995}
}
We address the problem of nding the parameter settings that will result in optimal performance of a given learning algorithm using a particular dataset as training data. We describe a \wrapper" method, considering determination of the best parameters as a discrete function optimization problem. The method uses bestrst search and crossvalidation to wrap around the basic induction algorithm: the search explores the space of parameter values, running the basic algorithm many times on training and… CONTINUE READING
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