Exact MSE Performance of the Bayesian MMSE Estimator for Classification Error
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Biomedicine is faced with difficult high-throughput small-sample classification problems, with classifier errors typically approximated using classical, though heuristically devised, resampling methods. A recently proposed Bayesian error estimator places the problem in a signal estimation framework in the presence of uncertainty, resulting in a minimum-mean-square error solution, where uncertainty is relative to the parameters of the feature-label distribution and conditioned on the observed sample. Here, we present the theoretical sample-conditioned MSE for Bayesian error estimators, demonstrating a unique advantage over resampling methods in that their mathematical framework naturally gives rise to a practical expected measure of performance given a fixed sample. 2011 IEEE.
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2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)