Article | REF: TE5255 V1

Kernel methods for statistical learning

Author: Stéphane CANU

Publication date: February 10, 2007

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4. Non-parsimonious core methods

The correct use of kernels is linked to a representation technique that enables us to move from an initial "functional" formulation (where the space of hypotheses is a set of functions of type ) to a second formulation, this time vector-based, showing, for each example, a coefficient representing the influence of this point in the solution. To illustrate this principle, let's take the example of interpolation splines.

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Non-parsimonious core methods