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In the proposed method, the multiple kernel scheme is applied to different meteorological elements.
As described above, a multiple kernel scheme does not always improve the performance [19].
Therefore, in order to merge such heterogeneous features, the proposed method introduces the multiple kernel scheme into the ordinal regression.
Furthermore, the proposed method adopts sampling of the training data and effectively uses the remaining data for estimating the optimal parameters of the multiple kernel scheme.
This means that we separately use training data for performing KDA-based ordinal regression and the combination parameter settings in the multiple kernel scheme to keep the robustness.
The proposed method tries to introduce the multiple kernel scheme into the KDA-based ordinal regression to merge multiple meteorological elements.
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Instead of learning one kernel, multiple kernel learning assumes the kernel is comprised of a linear combination of multiple predefined base kernels.
Multiple kernel learning (MKL, see [28]) was used to combine the kernels as input for IOKR.
Many researchers have studied multiple kernel learning (MKL) algorithms [19 21].
Unlike the traditional Multiple Kernel Learning (MKL) with the implicit kernels, Multiple Empirical Kernel Learning (MEKL) explicitly maps the original data space into multiple feature spaces via different empirical kernels.
They proposed an algorithm based on multiple empirical kernel which maps data explicitly in multiple kernel spaces.
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multiple kernel discriminant
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