Fast Leave-One-Out Cross-Validation Algorithm for Extreme Learning Machine

Abstract

Abstract: Leaveoneout crossvalidation has proved to be near capable of giving the unbiased estimation of the generalization performance of statistical models, and thus can provide a reliable criterion for model selection and comparison. For this reason, the current paper presented a fast leaveoneout crossvalidation algorithm in the framework of extreme learning machines (ELMs) with respect to both regression and classification problems, which can avoid training explicitly and just has the complexity of O(N) for a data set with N points. The validity of the algorithm is also strictly proved. The simulations conducted on the artificial and realworld problems show the effectiveness and efficiency of the proposed algorithm.

Key words: extreme learning machine (ELM), leaveoneout, crossvalidation, computational complexity

CLC Number:

TP18
O234

LIU Xue-Yi-a, LI Ping-a, b , GAO Chuan-Hou-c. Fast Leave-One-Out Cross-Validation Algorithm for Extreme Learning Machine[J]. Journal of Shanghai Jiaotong University, 2011, 45(08): 1140-1145.

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