Article | REF: TE5255 V1

Kernel methods for statistical learning

Author: Stéphane CANU

Publication date: February 10, 2007 | Lire en français

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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 H ) 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.

    4.1 Interpolation splines

    In the framework of interpolating splines, we search in an EHNR H...

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