title: Neural tree density estimation for novelty detection.
abstract: In this paper, a neural competitive learning tree is introduced as a computationally attractive scheme for adaptive density estimation and novelty detection. The learning rule yields equiprobable quantization of the input space and provides an adaptive focusing mechanism capable of tracking time-varying distributions. It is shown by simulation that the neural tree performs reasonably well while being much faster than any of the other competitive learning algorithms.
authors.affiliation: Laboratoire d'Analyse et d'Architecture des Systèmes-CNRS, 31077 Toulouse, France.
authors.forename: D
authors.initials: D
authors.lastname: Martinez