Jesús Maudes

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Boosting is a set of methods for the construction of classifier ensembles. The differential feature of these methods is that they allow to obtain a strong classifier from the combination of weak classifiers. Therefore, it is possible to use boosting methods with very simple base classifiers. One of the most simple classifiers are decision stumps, decision(More)
We describe an artificial vision system used to recognize the Spanish car license plate numbers in raster images. The algorithm is designed to be independent of the distance from the car to the camera, the size of the plate number, the inclination and the light conditions. In the preprocessing steps, the algorithm takes a raster image as input and gives an(More)
Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers(More)
A SCADA-data based data mining approach to estimate wind turbine loads Prevalence of, risk factors for, and oxidative stress associated with Toxoplasma gondii antibodies among Egyptian asymptomatic blood donors Content independant metadata production as a machine learning problem Sahar Changuel and Nicolas Labroche
Ensemble methods take their output from a set of base predictors. The ensemble accuracy depends on two factors: the base classifiers accuracy and their diversity (how different are these base classifiers outputs from each other). An approach for increasing the diversity of the base classifiers is presented in this paper. The method builds some new features(More)
Grafted trees are trees that are constructed using two methods. The first method creates an initial tree, while the second method is used to complete the tree. In this work, the first classifier is an unpruned tree from a 10% sample of the training data. Grafting is a method for constructing ensembles of decision trees, where each tree is a grafted tree.(More)