Journal of Systems Engineering and Electronics ›› 2014, Vol. 25 ›› Issue (5): 895-900.doi: 10.1109/JSEE.2014.00103
• SOFTWARE ALGORITHM AND SIMULATION •
Yongping Zhao* and Kangkang Wang
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.
Yongping Zhao and Kangkang Wang. Fast cross validation for regularized extreme learning machine[J]. Journal of Systems Engineering and Electronics, 2014, 25(5): 895-900.
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