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This paper lies within the domain of learning algorithms based on kernels of Support Vector Machines. A kernel is constructed over the discrete structure of absolute orders of magnitude spaces. This kernel is based on an explicit function, defined from the space of k-tuples of qualitative labels to a feature space, which captures the remoteness between the(More)
This paper lies within the domain of supervised learning algorithms based on neural networks whose architecture corresponds to radial basis functions. A methodology to use RBF when the descriptors of the patterns are given by means of their orders of magnitude is developed. A qualitative distance is constructed over the discrete structure of absolute orders(More)