Model selection for linear classifiers using Bayesian error estimation
We propose to use the Bayesian error estimator (BEE) for classifier model selection. We show that the BEE rule speeds up model selection by an order of magnitude. We show that the BEE rule selects a better model than cross-validation. We propose an approximation rule of the BEE for multi-label classification problems.
linear classifier bayesian error estimator logistic regression support vector machine regularization