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This paper discusses the design of binary image operators from training data and its relation to Boolean function learning. An extended version of the incremental splitting of intervals (ISI) algorithm for Boolean function learning and some improvements and heuristics to reduce its processing time are proposed. Some examples illustrate the application of(More)
We study chance-constrained problems in which the constraints involve the probability of a rare event. We discuss the relevance of such problems and show that the existing sampling-based algorithms cannot be applied directly in this case, since they require an impractical number of samples to yield reasonable solutions. Using a Sample Average Approximation(More)
In this paper, we call finite dynamical system (FDS) a discrete time and finite range dynamical system. A FDS is called a finite lattice dynam-ical system (FLDS) when its transition function is defined on a finite lattice. Hence, the transition function of a FLDS is a lattice operator and can be represented by an union of sup-generating operators,(More)
Resumen In this work, we propose a methodology to calibrate a dependent failure model to compute the reliability in a telecommunication network. We use the Marshall-Olkin (MO) co-pula model, which captures failures that arise simultaneously in groups of links. In practice , this model is difficult to calibrate because it requires the estimation of a number(More)
In this work, we propose a methodology for calibrating a dependent failure model to compute the reliability in a telecommunication network. We use the Marshall-Olkin (MO) copula model, which captures failures that arise simultaneously in groups of links. In practice, this model is difficult to calibrate because it requires the estimation of a number of(More)