kernel methods

Kernel methods (e.g. support vector machines for classification or Gaussian processes for regression) are based on constructing (and often inverting) a similarity matrix over the training and test sets, so they are generally considered appropriate in the small data regime. We list here challenges where kernel methods turned to be competitive.

See kernel method on Wikipedia.

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