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	Provedor de dados Repositório Alice (2) Autor DOMPIERI, M. H. G. (2) MACIEL, R. J. S. (2) MATOS, L. N. (2) SILVA, M. A. S. da (2) Palavra-chave Correspondence analysis (2) Exploratory spatial analysis (2) Similarity coefficients (2) Alto Taquari River Basin (1) Alto Taquari river (1) Análise espacial (1) Artificial neural network (1) Bacia do Alto Taquari (1) Self-organizing maps (1) Thematic maps (1) Zoneamento (1) Zoning (1) Mais... Tipo do documento Anais e Proceedings de eventos (1) Artigo de periódico (1) Ano 2018 (1) 2017 (1) País Brazil (2) Idioma Inglês (1) Português (1)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última A neural qualitative approach for automatic territorial zoning. Repositório Alice MACIEL, R. J. S.; SILVA, M. A. S. da; MATOS, L. N.; DOMPIERI, M. H. G.. This article presents the application of the Self-Organizing Maps (SOM) as an exploratory tool for automatic territorial zoning by combining the handle of categorical data and the other for automatic clustering. The SOM online learning algorithm had been chosen to treat categorical data by using the dot product method and the Sorense-Dice binary similarity coefficient. To automatically perform a spatial clustering, an adaptation of the automatic clustering Costa-Netto algorithm had been also proposed. The correspondence analysis had been used to examine the profiles of each homogeneous zones. To explore the approach it has been performed the territorial zoning of the Alto Taquari River Basin, Brazil, using as input data a set of thematic maps. The results... Tipo: Anais e Proceedings de eventos Palavras-chave: Bacia do Alto Taquari; Zoneamento; Análise espacial; Self-organizing maps; Exploratory spatial analysis; Similarity coefficients; Alto Taquari River Basin; Correspondence analysis; Zoning; Thematic maps. Ano: 2017 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1085890 Automatic environmental zoning with self-organizing maps. Repositório Alice SILVA, M. A. S. da; MACIEL, R. J. S.; MATOS, L. N.; DOMPIERI, M. H. G.. This article presents the application of the Self-Organizing Maps (SOM) as an exploratory tool for automatic environmental zoning by combining the handle of categorical data and the other for automatic clustering. The SOM online learning algorithm had been chosen to treat categorical data by using the dot product method and the Sorense-Dice binary similarity coefficient. To automatically perform a spatial clustering, an adaptation of the automatic clustering Costa-Netto algorithm had been also proposed. The correspondence analysis had been used to examine the profiles of each homogeneous zones. To explore the approach it has been performed the environmental zoning of the Alto Taquari River Basin, Brazil, using as input data a set of thematic maps. The... Tipo: Artigo de periódico Palavras-chave: Artificial neural network; Exploratory spatial analysis; Similarity coefficients; Alto Taquari river; Correspondence analysis. Ano: 2018 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1103941 Registros recuperados: 2 Primeira ... 1 ... Última

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