http://www.cnr.it/ontology/cnr/individuo/prodotto/ID77775
Identifying Clusters Using Growing Neural Gas: First Results (Contributo in atti di convegno)
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- Label
- Identifying Clusters Using Growing Neural Gas: First Results (Contributo in atti di convegno) (literal)
- Anno
- 2009-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1007/978-3-642-04274-4_56 (literal)
- Alternative label
Rizzo Riccardo, Urso Alfonso (2009)
Identifying Clusters Using Growing Neural Gas: First Results
in International Conference on Artificial Neural Networks, ICANN 2009, Liamassol
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Rizzo Riccardo, Urso Alfonso (literal)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
- Artificial Neural Networks - ICANN 2009 - 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part I (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
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- Google Scholar (literal)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Titolo
- Identifying Clusters Using Growing Neural Gas: First Results (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- 978-3-642-04273-7 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
- Cesare Alippi, Marios Polycarpou, Christos Panayiotou, Georgios Ellinas (literal)
- Abstract
- Growing Neural Gas is a self organizing network capable to build a lattice of neural unit that grows in the input pattern manifold. The structure of the obtained network often is not a planar graph and can be not suitable for visualization; cluster identification is possible only if a set of not connected subgraphs are produced. In this work we propose a method to select the neural units in order to extract the information on the pattern clusters, even if the obtained network graph is connected. The proposed method creates a new structure called Labeling Network (LNet) that repeats the topology of the GNG network and a set of weights to the links of the neuron graph. These weights are trained using an anti-Hebbian algorithm obtaining a new structure capable to label input patterns according to their cluster. (literal)
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