Mauricio Orozco-Alzate

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—Dissimilarities can be a powerful way to represent objects like strings, graphs and images for which it is difficult to find good features. The resulting dissimilarity space may be used to train any classifier appropriate for feature spaces. There is, however, a strong need for dimension reduction. Straightforward procedures for prototype selection as well(More)
When asymmetric dissimilarity measures arise, asymmetry correction methods such as averaging are used in order to make the matrix symmetric. This is usually needed for the application of pattern recognition procedures, but in this way the asymmetry information is lost. In this paper we present a new approach to make use of the asymmetry information in(More)
Spectral content of seismic signals contains essential information for discriminating different classes of volcanic earthquakes. Such an information is largely redundant; therefore, a reduce number of spectral regions may provide almost the same description of the original events. By reducing the number of bands considered, the amount of data to be(More)
—The automated classification of seismic volcanic signals has been faced with several different pattern recognition approaches. Among them, hidden Markov models (HMMs) have been advocated as a cost-effective option having the advantages of a straightforward Bayesian interpretation and the capacity of dealing with seismic sequences of different lengths. In(More)
Even though hidden Markov models (HMMs) have been used for the automatic classification of volcanic earthquakes, their usage has been so far limited to the Bayesian scheme. Recently proposed alternatives , proven in other application scenarios, consist in building HMM-induced vector spaces where discriminative classification techniques can be applied. In(More)
Traditional techniques for monitoring wildlife populations are temporally and spatially limited. Alternatively , in order to quickly and accurately extract information about the current state of the environment , tools for processing and recognition of acoustic signals can be used. In the past, a number of research studies on automatic classification of(More)
—Seismic events in and around volcanos, like tremors, earth quakes, ice quakes and strokes of lightning, are usually observed by multiple stations. The question rises whether classifiers trained for one seismic station can be used for classifying observations by other stations, and, moreover, whether a combination of station signals improves the(More)