Data/hora: 07/06/2024 03:39:16

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	16/08/1996	
Data da última atualização: 	03/08/2007	
Autoria: 	COSTA, L. da F.; JOO, J. M.; KOBERLE, R.	
Título: 	Distance-discriminator neural networks for classification and pattern recognition.	
Ano de publicação: 	1993	
Fonte/Imprenta: 	In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO GRÁFICA E PROCESSAMENTO DE IMAGENS, 6., 1993, Recife. Anais... Recife: SBC: UFPe, 1993.	
Páginas: 	p.221-229.	
Idioma: 	Inglês	
Notas: 	Evento conhecido como SIBGRAPI. Editado por Luiz H. de Figueiredo e Jonas de Miranda Gomes.	
Conteúdo: 	Ditance-Discriminator Neurous - DDNs and their combination in Distance-Discriminator Neural Networks - DDNNs are proposed and discussed. DDNs, based on distance metric concepts, are able to discriminate whether a given point (x,y) belongs to a closed region such as diamond-, rectaugle- and ellipse-bound regions, which are tasks traditionally performed by perceptrons. DDNs can also be straighforwardly modified in order to discriminate hollow regions having as other boundaries the above mentioned geonetrical figures or even combinations of them. The principal advantage of DDNNs over perceptrons is a substantial reduction of execution time and/or the ainount of required hardware operators: many polygonal classification regions which would otherwise demand largeperceptron structures can be discriminated with only a few DDNNs. DDNNs can also be easily programmed by design or automatically with the help of the hough transform. Such issues as well as the relative advantages of DDNNs and perceptrons and a complete application example are presented and discussed in the present paper.	
Categoria do assunto: 	--	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (3858-1) - ADD - PL - -- - 006.6 - SIM - 1994.00004 - DIS