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This tutorial paper introduces direct kernel least squares support vector machines, where traditional ridge regression is applied directly on the kernel transformed data, rather than using the primal dual formulation. A direct kernel method can be any regression model, where the kernel is considered as a data pre-processing step. The emphasis of the paper is that such direct kernel methods often require kernel centering in order to work. A heuristic formula for the regularization parameter is proposed based on preliminary scaling experiments.