Antonio Artés-Rodríguez

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In this paper, we present a novel scheme for linear feature extraction in classification. The method is based on the maximization of the mutual information (MI) between the features extracted and the classes. The sum of the MI corresponding to each of the features is taken as an heuristic that approximates the MI of the whole output vector. Then, a(More)
In this communication, we generalize the Support Vector Machines (SVM) for regression estimation and function approximation to multi-dimensional problems. We propose a multi-dimensional Support Vector Regressor (MSVR) that uses a cost function with a hyperspherical insensitive zone, capable of obtaining better predictions than using an SVM independently for(More)
Crowdsourcing has been proven to be an effective and efficient tool to annotate large data-sets. User annotations are often noisy, so methods to combine the annotations to produce reliable estimates of the ground truth are necessary. We claim that considering the existence of clusters of users in this combination step can improve the performance. This is(More)
An iterative block training method for support vector classifiers (SVCs) based on weighted least squares (WLS) optimization is presented. The algorithm, which minimizes structural risk in the primal space, is applicable to both linear and nonlinear machines. In some nonlinear cases, it is necessary to previously find a projection of data onto an(More)
OBJECTIVE To evaluate the long-term stability of International Classification of Diseases-10th revision bipolar affective disorder (BD) in multiple settings. METHOD A total of 34 368 patients received psychiatric care in the catchment area of a Spanish hospital (1992-2004). The analyzed sample included patients aged > or =18 years who were assessed on >(More)
In this paper we propose an Iterative Re-Weighted Least Square procedure in order to solve the Support Vector Machines for regression and function estimation. Furthermore, we include a new algorithm to train Support Vector Machines, covering both the proposed approach instead of the quadratic programming part and the most advanced methods to deal with large(More)
We consider the problem of target location estimation in the context of large scale, dense sensor networks. We model the probability of detection in each sensor, p/sub d/ as a function of the distance between the sensor and the target. Based on a binary (detection vs. no detection) information from each sensor and the model of p/sub d/, we propose two(More)
Attempted suicide appears to be a familial behavior. This study aims to determine the variables associated with family history of attempted suicide in a large sample of suicide attempters. The sample included 539 suicide attempters 18 years or older recruited in an emergency room. The two dichotomous dependent variables were family history of suicide(More)
We present a new approach to nonparametric spectral estimation on the basis of the support vector method (SVM). A reweighted least squares error formulation avoids the computational limitations of quadratic programming. The application to a synthetic example and to a digital communication problem shows the robustness of the SVM spectral analysis algorithm.