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Abstract: Integrated Mean Squared Error (IMSE) is a version of the usual mean squared error criterion, averaged over all possible training sets of a given size. If it could be observed, it could be used to determine optimal network complexity or optimal data subsets for efficient training. We show that two common methods of cross-validating average squared error deliver unbiased estimates of IMSE, converging to IMSE with probability one. These estimates thus make possible approximate IMSE-based choice of ... (Update)
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...other four fifths are used for training. Thus every scribble in the file is used once for testing and four times for training. Plutowski [132] has shown that cross validation estimates Integrated Mean Squared Error (IMSE) within a constant due to noise. IMSE is a version of...
.... Techniques exist to alleviate this problem, such as using a cross validation set of samples to determine when to terminate optimization [100], or adding noise to the optimization [135] However, over fitting has not been a problem in the experiments reported in this...
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BibTeX entry: (Update)
Plutowski, Mark E., Shinichi Sakata, and Halbert White. Cross-Validation Estimates IMSE. In Advances in Neural Information Processing Systems 6, eds. J. D. Cowan, G. Tesauro, and J. Alspector, J. San Mateo, California: Morgan Kaufmann, 1994. http://citeseer.ist.psu.edu/221416.html More
@misc{ mark94crossvalidation,
author = "P. Mark and E. Shinichi and S. Halbert",
title = "Cross-Validation Estimates IMSE",
text = "Plutowski, Mark E., Shinichi Sakata, and Halbert White. Cross-Validation
Estimates IMSE. In Advances in Neural Information Processing Systems 6,
eds. J. D. Cowan, G. Tesauro, and J. Alspector, J. San Mateo, California:
Morgan Kaufmann, 1994.",
year = "1994",
url = "citeseer.ist.psu.edu/221416.html" }
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