An adaptive classifier for high dimensional image data anda small training sample set

Neste trabalho é testado um classificador adaptativo que visa suavizar os efeitos causados por um número insuficiente de amostras de treinamento, fato este que pode degradar severamente a acurácia dos resultados obtidos por um classificador paramétrico utilizando dados com dimensão alta. O classificador adaptativo adiciona amostras semi-rotuladas ao conjunto das amostras de treinamento com o objetivo de reduzir os efeitos causados pelo pequeno número de amostras. O efeito das amostras semi-rohlladas é controlado por meio de um peso menor do que o peso atribuído as amostras originais. Os experimentos desenvolvidos mostram que este procedimento é eficiente na redução dos efeitos do fenômeno de Hughes contribuindo para aumentar a acurácia da imagem temática produzida.In this paper, we test a self-leaming and self-improving adaptive classifier to mitigate the problem of small training sample size that can severely affect the accuracy of the results produced by a parametric classifier employing high dimensional image data. The adaptive classifier mitigates the small training sample size by adding semi-labeled samples to the training set. In order to control the influence of semi-labeled samples, the proposed method assigns full weight to the training samples and reduced weight to semi-labeled samples. Experiments show that this procedure is effective in mitigating the Hughes phenomenon and increasing therefore the accuracy ofthe resulting thematic ma

Criação de um Sistema de Informações Geográfico com Espacialização das Unidades Básicas de Saúde e Escolas do Município de Rio Grande

The city of Rio Grande is in significant growth, both in the economic area and in population, and thus realizes the need for guidance for the population increases due to the development of the shipbuilding center of the city. These people, often from other states along with their families do not always know the city and its resources. With this, the present work aims to facilitate the recognition by the population through a GIS location and satisfaction information from two main services: health and education. This will generate a web mapping in order to propose to the user greater knowledge about the city you live in and support decision making at the time of going to a basic health unit or enroll your child in a school, providing the lowest way and the nearest locations. It also aims to facilitate the sharing of data between geo, City Hall and population, so that everyone has knowledge of the environment we live in and so they have a tool to aid decision and also having standardized information between offices, to facilitate discussion therebetween. So there will be disagreement at the time of consultation or crossover of information between the different municipal departments.Pages: 3994-400