Jorge de la Calleja

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In this paper we present an experimental study of the performance of three machine learning algorithms applied to the difficult problem of galaxy classification. We use the Naive Bayes classifier, the rule-induction algorithm C4.5 and a recently introduced classifier named random forest (RF). We first employ image processing to standardize the images,(More)
Planning a collision-free path for a rigid or articulated robot to move from an initial to a final configuration in a static environment is a central problem in robotics and has been extensively addressed over the last. The complexity of the problem is NP-hard (Latombe, 1991). There exist several family sets of variations of the basic problem, that consider(More)
In this paper we present an experimental study of machine learning and image analysis for performing automated morphological galaxy classification. We have used a neural network, and a locally weighted regression method, and also we implemented homogeneous ensembles of classifiers. The ensemble of neural networks was created using the bagging ensemble(More)
In this paper we present an automated method for classifying astronomical objects in multi-spectral widefield images. The classification method is divided into three main stages. The first one consists of locating and matching the astronomical objects in the multi-spectral images. In the second stage we create a compact representation of each object(More)
In this paper we present an automated method for classifying astronomical objects in multispectral wide-field images. The method is divided into three main tasks. The first one consists of locating and matching the objects in the multispectral images. In the second task we create a new representation for each astronomical object using its multispectral(More)
In this paper we present an experimental study of machine learning from imbalanced data sets applied to the difficult problem of astronomical object classification in multi-spectral wide-field images. The imbalanced data set problem is very common in several domains, and occurs when there are many more examples of some classes than others; therefore,(More)