Abstract
The empirical risks of regression models are not accurate since they are evaluated from the finite number of samples. In this context, we investigate the confidence intervals for the risks of regression models, that is, the intervals between the expected and empirical risks. The suggested method of estimating confidence intervals can provide a tool for predicting the performance of regression models.
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© 2006 Springer-Verlag Berlin Heidelberg
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Koo, I., Kil, R.M. (2006). Confidence Intervals for the Risks of Regression Models. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_84
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DOI: https://doi.org/10.1007/11893028_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
Online ISBN: 978-3-540-46480-8
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