Journal of Systems Engineering and Electronics ›› 2014, Vol. 25 ›› Issue (5): 895-900.doi: 10.1109/JSEE.2014.00103

• SOFTWARE ALGORITHM AND SIMULATION • Previous Articles     Next Articles

Fast cross validation for regularized extreme learning machine

Yongping Zhao* and Kangkang Wang   

  1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
  • Online:2014-10-23 Published:2010-01-03


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.