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Are there any good resources regarding how to design kernels for regression problems, specifically time-series regression type of problem. I am finding the choice of a kernel for regression extremely unintuitive.

For a classification problem with hinge loss, the idea is that the kernel should map similar examples closely while keep dis-similar examples apart. What is the equivalent notion for the ILF loss used in SVR?

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