Learning from given patterns is realized by learning from their appropriate representations.
This is usually practiced either by defining a set of features or by measuring proximities
between pairs of objects. Both approaches are problem dependent and aim at the
construction of some representation space, where discrimination functions can be defined.
In most situations, some feature reduction or prototype selection is mandatory. In this paper,
a pairwise selection for creating a suitable representation space is proposed. To determine …