Journal Article10.1109/72.655027
Cross-validation with active pattern selection for neural-network classifiers
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TL;DR: In CV/APS, the contribution of the training patterns to network learning is estimated and this information is used for active selection of CV patterns, and the computational cost of CV can be drastically reduced with only small or no errors.
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Abstract: We propose a new approach for leave-one-out cross-validation of neural-network classifiers called "cross-validation with active pattern selection" (CV/APS). In CV/APS, the contribution of the training patterns to network learning is estimated and this information is used for active selection of CV patterns. On the tested examples, the computational cost of CV can be drastically reduced with only small or no errors.
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References
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TL;DR: The classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion to provide answers to many practical problems of statistical model fitting.
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Information Theory and an Extension of the Maximum Likelihood Principle
Hirotugu Akaike
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TL;DR: In this paper, it is shown that the classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion.
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Cross-Validatory Choice and Assessment of Statistical Predictions
TL;DR: In this article, a generalized form of the cross-validation criterion is applied to the choice and assessment of prediction using the data-analytic concept of a prescription, and examples used to illustrate the application are drawn from the problem areas of univariate estimation, linear regression and analysis of variance.
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Cross-Validatory Choice and Assessment of Statistical Predictions (With Discussion)
TL;DR: A generalized form of the cross-validation criterion is applied to the choice and assessment of prediction using the data-analytic concept of a prescription.
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