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In recent years, biomedicine has been faced with difficult high-throughput small-sample classification problems. In such settings, classifier error estimation becomes a critical issue because training and testing must be done on the same data. A recently proposed error estimator places the problem in a signal estimation framework in the presence of uncertainty, permitting a rigorous solution optimal...
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