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Abstract: We address the problem of finding 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-first 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 ... (Update)
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BibTeX entry: (Update)
Kohavi, R. & John, G. (1995), Automatic parameter selection by minimizing estimated error, in A. Prieditis & S. Russell, eds, Machine Learning: Proceedings of the Twelfth International Conference, Morgan Kaufmann, pp. 304--312. http://citeseer.ist.psu.edu/kohavi95automatic.html More
@inproceedings{ kohavi95automatic,
author = "Ron Kohavi and George John",
title = "Automatic Parameter Selection by Minimizing Estimated Error",
booktitle = "Machine Learning: Proceedings of the Twelfth International Conference",
publisher = "Morgan Kaufmann",
editor = "Armand Prieditis and Stuart Russell",
pages = "304--312",
year = "1995",
url = "citeseer.ist.psu.edu/kohavi95automatic.html" }
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