Neural tree density estimation for novelty detection
D Martinez - IEEE Transactions on Neural Networks, 1998 - ieeexplore.ieee.org
IEEE Transactions on Neural Networks, 1998•ieeexplore.ieee.org
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.
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.
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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