Autmatic Parameter Selection by Minimizing Estimated Error


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 best-rst search and cross-validation to wrap… (More)