Optimal convex error estimators for classification

@article{Sima2006OptimalCE,
  title={Optimal convex error estimators for classification},
  author={Chao Sima and Edward R. Dougherty},
  journal={Pattern Recognition},
  year={2006},
  volume={39},
  pages={1763-1780}
}
A cross-validation error estimator is obtained by repeatedly leaving out some data points, deriving classifiers on the remaining points, computing errors for these classifiers on the left-out points, and then averaging these errors. The 0.632 bootstrap estimator is obtained by averaging the errors of classifiers designed from points drawn with replacement and then taking a convex combination of this “zero bootstrap” error with the resubstitution error for the designed classifier. This gives a… CONTINUE READING
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