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Titolo:
Rough sets as a front end of neural-networks texture classifiers
Autore:
Swiniarski, RW; Hargis, L;
Indirizzi:
San Diego State Univ, Dept Math & Comp Sci, San Diego, CA 92182 USA San Diego State Univ San Diego CA USA 92182 Sci, San Diego, CA 92182 USA
Titolo Testata:
NEUROCOMPUTING
, volume: 36, anno: 2001,
pagine: 85 - 102
SICI:
0925-2312(200102)36:<85:RSAAFE>2.0.ZU;2-1
Fonte:
ISI
Lingua:
ENG
Keywords:
rough sets; neural networks; feature extraction;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
41
Recensione:
Indirizzi per estratti:
Indirizzo: Hargis, L PMB 189,6755 Mira Mesa Blvd Ste,123, San Diego, CA 92121 USA PMB 189,6755 Mira Mesa Blvd Ste,123 San Diego CA USA 92121 USA
Citazione:
R.W. Swiniarski e L. Hargis, "Rough sets as a front end of neural-networks texture classifiers", NEUROCOMPUT, 36, 2001, pp. 85-102

Abstract

The paper. describes an application of rough sets method to feature selection and reduction as a front end of neural-network-based texture images recognition. The methods applied include singular-value decomposition (SVD) for feature extraction, principal components analysis (PCA) for feature projection acid reduction, and rough sets methods for feature selection and reduction. For texture classification the feedforward backpropagation neural networks were applied. The. numerical experiments show the ability of rough sets to select reduced set of pattern's features (minimizing the pattern size), while providing better generalization of neural-network texture classifiers. (C) 2001 Published by Elsevier Science B.V.

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Documento generato il 03/04/20 alle ore 09:20:50