Germán Castellanos

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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)
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)
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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