DocumentCode
1340555
Title
Neural tree density estimation for novelty detection
Author
Martinez, Dominique
Author_Institution
Lab. d´´Anal. et d´´Archit. des Syst., CNRS, Toulouse, France
Volume
9
Issue
2
fYear
1998
fDate
3/1/1998 12:00:00 AM
Firstpage
330
Lastpage
338
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
Keywords
adaptive estimation; data structures; maximum entropy methods; neural nets; quantisation (signal); trees (mathematics); unsupervised learning; adaptive density estimation; adaptive multivariable histogram; competitive learning tree; equiprobable quantization; learning rule; maximum entropy; neural tree density estimation; novelty detection; sequential detection; time-varying distributions; tree data structure; unsupervised learning; Clustering algorithms; Computational modeling; Convergence; Covariance matrix; Histograms; Iterative algorithms; Lattices; Neurons; Partitioning algorithms; Quantization;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.661127
Filename
661127
Link To Document