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CLUSTERING ON DISSIMILARITY REPRESENTATIONS FOR DETECTING MISLABELLED SEISMIC SIGNALS AT NEVADO DEL RUIZ VOLCANO
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and objective, the use of supervised learning algorithms has been explored; particularly classifiers built in dissimilarity spaces. Nonetheless, the performance of such learning methods is subject to the availability of a representative and a priori well classified training sets. To detect mislabeled events, the use of clustering techniques on the dissimilarity representations is proposed. Our experiments,
performed on re-analyzed seismic signals, show a significant improvement respect to recognition accuracies for the original data sets.
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