Abstract
We present a fast and accurate algorithm–reduced kernel extreme learning machine (Reduced-KELM). It randomly selects a subset from given dataset, and uses \(\mathcal{K}(X,\tilde{X})\) in place of \(\mathcal{K}(X,X)\). The large scale kernel matrix with size of n×n is reduced to \(n\times \tilde{n} \), and the time-consuming computation for inversion of kernel matrix is reduced to \(O(\tilde{n}^3) \) from O(n 3) where \(\tilde{n} \ll n \). The experimental results show that Reduced-KELM can perform at a similar level of accuracy as KELM and at the same time being significantly faster than KELM.
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Deng, W., Zheng, Q., Zhang, K. (2013). Reduced Kernel Extreme Learning Machine. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_6
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DOI: https://doi.org/10.1007/978-3-319-00969-8_6
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