Article | REF: TE5255 V1

Kernel methods for statistical learning

Author: Stéphane CANU

Publication date: February 10, 2007

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1. Context

Today, systems designed to check whether a banknote is true or false, to recognize a person from the sound of his or her voice or to detect outliers in a database incorporate algorithms derived from statistical learning theory, and in particular a kernel machine as a decision tool. The programming of these systems uses a set of observation-label pairs to elaborate a decision rule. This is known as statistical learning or programming by example.

The base of examples used is a sample of n realizations (x i , y i ),

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