Unsupervised learning for neural trees

Abstract

A self-organizing neural tree is studied. The neural tree is suited to hierarchical classifications. Unsupervised learning algorithms have been developed for the neural tree. A simulation study indicated that the vectors represented by the nodes of the tree tend to approximate the probability of the sample distribution. The neural tree has been applied to speech recognition and image coding. Promising results have been obtained.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>