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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 presents a method for evaluating qualitative orders of magnitude information in multi-attribute decision-making. It allows the selection of an alternative from among a set of alternatives. These are characterized by having all descrip-tors defined in orders of magnitude. A representation for the different alternatives by means of k-dimensional(More)