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| Fast Leave-One-Out Cross-Validation Algorithm for Extreme Learning Machine |
| LIU Xue-Yi-a, LI Ping-a, b , GAO Chuan-Hou-c |
| (a. School of Aeronautics and Astronautics; b. Institute of Industrial Process Control;c. Department of Mathematics, Zhejiang University, Hangzhou 310027, China) |
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Abstract Leaveoneout crossvalidation 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 leaveoneout crossvalidation 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 realworld problems show the effectiveness and efficiency of the proposed algorithm.
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Received: 15 March 2011
Published: 30 August 2011
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