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)
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)
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)
—This paper presents a new approach to auto-regres-sive and moving average (ARMA) modeling based on the support vector method (SVM) for identification applications. A statistical analysis of the characteristics of the proposed method is carried out. An analytical relationship between residuals and SVM-ARMA coefficients allows the linking of the fundamentals… (More)
In this paper, the problem of simultaneously approximating a function and its derivatives is formulated within the Support Vector Machine (SVM) framework. First, the problem is solved for a one-dimensional input space by using the ε-insensitive loss function and introducing additional constraints in the approximation of the derivative. Then, we extend the… (More)
An iterative reweighted least squares (IRWLS) procedure recently proposed is shown to converge to the support vector machine solution. The convergence to a stationary point is ensured by modifying the original IRWLS procedure.
We consider the problem of binary distributed detection in the context of large-scale, dense sensor networks. We propose to model the probability of detection in each sensor, p d , as a function of the distance between the sensor and the source or target to be detected. We derive the Bayesian fusion rule under that model. We also derive , using the… (More)
In this paper we tackle the problem of detecting sources of combustion in high definition multispectral Medium Wavelength InfraRed (MWIR) (3–5µm) images. We present a novel approach to this problem consisting in processing the images block-wise using a new technique that we call Supervised Principal Component Analysis (SPCA) to get the components of these… (More)