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Aprendizado supervisionado incremental de redes bayesianas para mineração de dados
(Universidade Federal de São Carlos, 2007-08-29) Yoshida, Murilo Lacerda; Hruschka Júnior, Estevam Rafael; https://lattes.cnpq.br/2097340857065853; https://lattes.cnpq.br/5724332859332178
The objective of this work is to introduce two algorithms for supervised Bayesian network incremental learning, AIP (Algorithm for simple Bayesian network numerical parameters supervised incremental learning) and ABC (Algorithm for Bayesian network supervised incremental learning in layers). In order to develop these algorithms we studied relevant works about the Bayesian networks concepts, the algorithms for supervised Bayesian network learning and the algorithms for incremental supervised Bayesian network learning. To improve the performance of the ABC algorithm, we studied the AD-Tree structure and implemented it on the algorithm. To measure the quality of the networks learned by the algorithms we used these networks learnt to classify a test set, resulting in the correct classification rate (ICC). To do that we studied the test set classification process and the propagation of evidences along the Bayesian network. The result of the studies is described on this work, along with the results and discussions about the experiments made with the introduced algorithms.
Filtragem de projeções tomográficas da ciência do solo utilizando Kalman e redes neurais
(Universidade Federal de São Carlos, 2007-08-29) Laia, Marcos Antonio de Matos; Cruvinel, Paulo Estevão; https://lattes.cnpq.br/7924553462118511; https://lattes.cnpq.br/7114274011978868
This work presents the space variant noise filtering of tomographic projections based on the Kalman filter. For development and filter selection it was evaluated different modalities of the Kalman filter, as well as included the use of Ascombe transform and neural network. Results were analyzed by means of Improvement in Signal to Noise Ratio (ISNR) measurements, which were obtained in a region of interest (ROI) on the resultant images, reconstructed with the use of a backprojection algorithm. In this context the results qualified the unscented Kalman filter with a neural network as the best configuration for filtering of soil tomographic projections.