NIPS 2011 Big Learning - Algorithms, Systems, & Tools Workshop: Fast Cross-Validation...

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Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011

Invited Talk: Fast Cross-Validation via Sequential Analysis by Tammo Kruger

Abstract: 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.



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