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Neural tree density estimation for novelty detection

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1 Author(s)
Martinez, D. ; Lab. d''Anal. et d''Archit. des Syst., CNRS, Toulouse, France

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

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Neural Networks, IEEE Transactions on  (Volume:9 ,  Issue: 2 )