Roberto Díaz-Morales

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The number of computers, tablets and smartphones is increasing rapidly, which entails the ownership and use of multiple devices to perform online tasks. As people move across devices to complete these tasks, their identities becomes fragmented. Understanding the usage and transition between those devices is essential to develop efficient applications in a(More)
In recent years the number of cores in computers has increased considerably, opening new lines of research to adapt classical techniques of machine learning to a parallel scenario. In this paper, we have developed and implemented with the multi-platform application programming interface OpenMP a method to train Semiparametric Support Vector Machines relying(More)
In this paper, we present an approach to deal with the maximization of the approximate median discovery significance (AMS) in high energy physics. This paper proposes the maximization of the Weighted AUC as a criterion to train different models and the subsequent creation of an ensemble that maximizes the AMS. The algorithm described in this paper was our(More)
Logistic models, comprising a linear filter followed by a nonlinear memoryless sigmoidal function, are often found in practice in many fields, e.g., biology, probability modelling, risk prediction, forecasting, signal processing, electronics and communications, etc., and in many situations a real time response is needed. The online algorithms used to update(More)
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