Neural tree density estimation for novelty detection.
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.
Martinez
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Laboratoire d'Analyse et d'Architecture des Systèmes-CNRS, 31077 Toulouse, France.