中文摘要：

A method for fast l-fold cross validation is proposed for the regularized extreme learning machine(RELM). The computational time of fast l-fold cross validation increases as the fold number decreases, which is opposite to that of naive l-fold cross validation. As opposed to naive l-fold cross validation, fast l-fold cross validation takes the advantage in terms of computational time, especially for the large fold number such as l > 20. To corroborate the efficacy and feasibility of fast l-fold cross validation,experiments on five benchmark regression data sets are evaluated.

英文摘要：

A method for fast 1-fold cross validation is proposed for the regularized extreme learning machine （RELM）. The computational time of fast l-fold cross validation increases as the fold number decreases, which is opposite to that of naive 1-fold cross validation. As opposed to naive l-fold cross validation, fast l-fold cross validation takes the advantage in terms of computational time, especially for the large fold number such as l 〉 20. To corroborate the efficacy and feasibility of fast l-fold cross validation, experiments on five benchmark regression data sets are evaluated.