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Kernel methods are used in machine learning to generalize linear techniques to nonlinear situations, especially SVMs, PCA, and GPs. Not to be confused with [kernel-smoothing], for kernel density estimation (KDE) and kernel regression.

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When you use a model that has been trained based on the RBF (Gaussian) kernel, do you need to store the entire training dataset to compute similarity features? If so, why doesn't this decrease the eff …
asked Sep 9 '17 by GingerBadger