Germán Castellanos

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Heuristical algorithms can reduce the computational complexity. Such methods require of some stopping criteria (cost function). Some of these cost functions are based on statistics like univariate and multivariate methods of analysis. Dimensional reduction techniques such as principal component analysis (PCA) allow to find a lower dimension transformed(More)
Hidden Markov models have shown promising results for identification of spike sources in Parkinson's disease treatment, e.g., for deep brain stimulation. Usual classification criteria consist in maximum likelihood rule for the recognition of the correct class. In this paper, we present a different classification scheme based in proximity analysis. For this(More)
IntroductIon In the treatment of children with fixed Cleft Lip and Palate (CLP), problems such as hipponasality and hypernasality, which are related to vocal emission and resonance, might appear. Nevertheless, according to the report presented in Castellanos et al. (2006), hy-pernasality is more frequently found than hipponasality (90% vs. 10%). The(More)
—A methodology based on acoustic analysis of digitized phonocar diogr aphic signals (PCG) is pr esented, or iented to detection of car diac mur mur s or iginated by valvular pathologies. Initially, a filtr ation system based on the wavelet tr ansfor m is developed to r educe the distur bances that usually appear in the acquisition stage, adjusting the sound(More)
Here, a lip posture recognition and segmentation system by means of tuning adjustable parameter models, is shown. For segmentation, deformable templates are used, and for feature extraction a variational model of lip contours is implemented. Both models are tuned by canonical genetic algorithms yielding performances up to 86.5% and 76.6% respectively.
Kernel Principal Component analysis is a nonlinear generalization of the popular linear multivariate analysis method. However, this method assumes that the observed data is independent, a disadvantage for many practical applications. In order to overcome this difficulty, the authors propose a combination of Kernel Principal Component analysis and hidden(More)
In this article, it is studied the usefulness of the support vector machines (SVM) algorithm in the active classification of voice records into the sets normal and pathologic. In practice, each one of the samples employed on the classifier training must be manually labelled by an specialist, increasing in this way the training cost. Thus, it is imperative(More)
In this article, it is studied the usefulness of the support vector machines (SVM) algorithm in the active classification of voice records into the sets normal and pathologic. In practice, each one of the samples employed on the classifier training must be manually labelled by an specialist, increasing in this way the training cost. Thus, it is imperative(More)
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