Milan Milosavljevic

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Collecting data for sentiment analysis in resource-limited languages carries a significant risk of sample selection bias, since the small quantities of available data are most likely not representative of the whole population. Ignoring this bias leads to less robust machine learning classifiers and less reliable evaluation results. In this paper we present(More)
In this paper we present a class of time series distance measures based on the difference of their cepstral transformations. We emphasise the convenience of the proposed distance measure in the cases when the time series can be treated as output of a linear system driven with a quasi-periodic stochastic signals. In order to illustrate the cepstral time(More)
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