Automated labeling for unsupervised neural networks: a hierarchical approach

In this paper a hybrid system and a hierarchical neural-net approaches are proposed to solve the automatic labeling problem for unsupervised clustering. The first method involves the application of nonneural clustering algorithms directly to the output of a neural net; and the second one is based on...

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Veröffentlicht in:IEEE transactions on neural networks Jg. 10; H. 1; S. 199 - 203
Hauptverfasser: Tagliaferri, R., Capuano, N., Gargiulo, G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.01.1999
Institute of Electrical and Electronics Engineers
Schlagworte:
Algorithms
Applied sciences
Automated
Clustering
Clustering algorithms
Electric, optical and optoelectronic circuits
Electronics
Exact sciences and technology
Hybrid systems
Labeling
Marking
Multi-layer neural network
Multilayers
Neural networks
Neurons
Organizations
Experimental result
Hybrid network
Descent method
Labelling
Pattern recognition
Neural network
Hierarchical system
Learning algorithm
Gradient method
Multilayer network
ISSN:1045-9227
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:In this paper a hybrid system and a hierarchical neural-net approaches are proposed to solve the automatic labeling problem for unsupervised clustering. The first method involves the application of nonneural clustering algorithms directly to the output of a neural net; and the second one is based on a multilayer organization of neural units. Both methods are a substantial improvement with respect to the most important unsupervised neural algorithms existing in the literature. Experimental results are shown to illustrate clustering performance of the systems.
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ObjectType-Correspondence-1
ISSN:1045-9227
DOI:10.1109/72.737509