Model selection for linear classifiers using Bayesian error estimation

作者：

Highlights：

• 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.

摘要

Highlights•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.

论文关键词：Logistic regression,Support vector machine,Regularization,Bayesian error estimator,Linear classifier

论文评审过程：Received 20 November 2014, Revised 6 March 2015, Accepted 2 May 2015, Available online 14 May 2015, Version of Record 16 July 2015.

论文官网地址：https://doi.org/10.1016/j.patcog.2015.05.005