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Titolo:
New model of self-organizing neural networks and its application in data projection
Autore:
Su, MC; Chang, HT;
Indirizzi:
Natl Cent Univ, Dept Comp Sci & Informat Engn, Taipei, Taiwan Natl Cent Univ Taipei Taiwan t Comp Sci & Informat Engn, Taipei, Taiwan Tamkang Univ, Dept Elect Engn, Taipei, Taiwan Tamkang Univ Taipei Taiwan amkang Univ, Dept Elect Engn, Taipei, Taiwan
Titolo Testata:
IEEE TRANSACTIONS ON NEURAL NETWORKS
fascicolo: 1, volume: 12, anno: 2001,
pagine: 153 - 158
SICI:
1045-9227(200101)12:1<153:NMOSNN>2.0.ZU;2-E
Fonte:
ISI
Lingua:
ENG
Soggetto:
MAPS; REDUCTION;
Keywords:
cluster analysis; projection algorithm; self-organizing feature map;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
21
Recensione:
Indirizzi per estratti:
Indirizzo: Su, MC Natl Cent Univ, Dept Comp Sci & Informat Engn, Taipei, Taiwan Natl Cent Univ Taipei Taiwan Sci & Informat Engn, Taipei, Taiwan
Citazione:
M.C. Su e H.T. Chang, "New model of self-organizing neural networks and its application in data projection", IEEE NEURAL, 12(1), 2001, pp. 153-158

Abstract

In this paper a new model of self-organizing neural networks is proposed, An algorithm called "double self-organizing feature map" (DSOM) algorithm is developed to train the novel model, By the DSOM algorithm the network will adaptively adjust its network structure during the learning phase so as to make neurons responding to similar stimulus have similar weight vectors and spatially move nearer to each other at the same time. The final network structure allows us to visualize high-dimensional data as a two-dimensionalscatter plot. The resulting representations allow a straightforward analysis of the inherent structure of clusters within the input data. One high-dimensional data set is used to test the effectiveness of the proposed neuralnetworks.

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Documento generato il 28/01/20 alle ore 03:03:30