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Neural tree density estimation for novelty detection

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D. MartinezAuthors Info & Claims

IEEE Transactions on Neural Networks, Volume 9, Issue 2

Pages 330 - 338

https://doi.org/10.1109/72.661127

Published: 01 March 1998 Publication History

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

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Rani AForesti GMicheloni C(2019)A neural tree for classification using convex objective functionPattern Recognition Letters10.1016/j.patrec.2015.08.01768:P1(41-47)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.patrec.2015.08.017
Wang SYu JLapira ELee J(2013)A modified support vector data description based novelty detection approach for machinery componentsApplied Soft Computing10.1016/j.asoc.2012.11.00513:2(1193-1205)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1016/j.asoc.2012.11.005
Filippone MSanguinetti G(2010)Information theoretic novelty detectionPattern Recognition10.1016/j.patcog.2009.07.00243:3(805-814)Online publication date: 1-Mar-2010
https://dl.acm.org/doi/10.1016/j.patcog.2009.07.002
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Neural tree density estimation for novelty detection
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image IEEE Transactions on Neural Networks

IEEE Transactions on Neural Networks Volume 9, Issue 2

March 1998

106 pages

ISSN:1045-9227

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IEEE Press

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Published: 01 March 1998

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Rani AForesti GMicheloni C(2019)A neural tree for classification using convex objective functionPattern Recognition Letters10.1016/j.patrec.2015.08.01768:P1(41-47)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.patrec.2015.08.017
Wang SYu JLapira ELee J(2013)A modified support vector data description based novelty detection approach for machinery componentsApplied Soft Computing10.1016/j.asoc.2012.11.00513:2(1193-1205)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1016/j.asoc.2012.11.005
Filippone MSanguinetti G(2010)Information theoretic novelty detectionPattern Recognition10.1016/j.patcog.2009.07.00243:3(805-814)Online publication date: 1-Mar-2010
https://dl.acm.org/doi/10.1016/j.patcog.2009.07.002
Chandola VBanerjee AKumar V(2009)Anomaly detectionACM Computing Surveys (CSUR)10.1145/1541880.154188241:3(1-58)Online publication date: 30-Jul-2009
https://dl.acm.org/doi/10.1145/1541880.1541882
Markou MSingh S(2003)Novelty detectionSignal Processing10.1016/j.sigpro.2003.07.01983:12(2499-2521)Online publication date: 1-Dec-2003
https://dl.acm.org/doi/10.1016/j.sigpro.2003.07.019

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Density peaks clustering algorithm based on kernel density estimation and minimum spanning tree

Mixture density estimation with group membership functions

Density estimation trees

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