How tracer objects can improve competitive learning algorithms in astronomy

The main objective of this paper is to discuss how the use of tracer objects in competitive learning can improve results in stellar classification. To do this, we work with a Kohonen network applied to a reduced sample of the Hipparcos Input Catalogue, which contains missing values. The use of synthetic stars as tracer objects allows us to determine the discrimination quality and to find the best final values of the cluster centroids, or neuron weights.