Please help transcribe this video using our simple transcription tool. You need to be logged in to do so.
With the increasing size of today's data sets, finding the right parameter configuration via cross-validation can be an extremely time-consuming task. In this paper we propose an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating underperforming candidates quickly and keeping promising candidates as long as possible the method speeds up the computation while preserving the capability of the full cross-validation. The experimental evaluation shows that our method reduces the computation time by a factor of up to 70 compared to a full cross-validation with a negligible impact on the accuracy.
Questions and AnswersYou need to be logged in to be able to post here.