938 resultados para multi-classification constrained-covariance regres


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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

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Dissertação para obtenção do Grau de Mestre em Engenharia Química e Bioquímica

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática

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Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

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We report a retrospective histopathological classification carried out under laboratory conditions by the method of Ridley & Jopling of 1,108 skin biopsies from patients clinically suspected of having leprosy from Bahia, Northeast Brazil.

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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

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Dissertation presented to obtain the Ph.D degree in Biochemistry, Structural Biochemistry

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Dissertação para obtenção do grau de Mestre em Engenharia Química e Bioquímica

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Dissertação para obtenção do Grau de Mestre em Engenharia Química e Bioquímica

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Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e Computadores

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Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de Computadores

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Dissertation presented to obtain the Ph.D degree in Computational Biology

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In the last years, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information. This huge amount of data might be hiding a vast geographical richness and therefore research needs to be conducted to explore their potential and use it in the solution of real world problems. In this study we conduct an exploratory analysis of data from the OpenStreetMap initiative. Using the Corine Land Cover database as reference and continental Portugal as the study area, we establish a possible correspondence between both classification nomenclatures, evaluate the quality of OpenStreetMap polygon features classification against Corine Land Cover classes from level 1 nomenclature, and analyze the spatial distribution of OpenStreetMap classes over continental Portugal. A global classification accuracy around 76% and interesting coverage areas’ values are remarkable and promising results that encourages us for future research on this topic.