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A probabilistic support vector machine (PSVM) is proposed for classification of data with uncertainties. Performance of the traditional SVM algorithm is very sensitive to uncertainties. The noises in input space will cause uncertainties of the mapping in feature space. The traditional SVM algorithm may not be effective when uncertainty is large. A new probabilistic optimization is proposed to determine...
Data uncertainty is common in real-world applications due to various causes, including imprecise measurement, network latency, outdated sources and sampling errors. These kinds of uncertainty have to be handled cautiously, or else the mining results could be unreliable or even wrong. In this paper, we propose a new rule-based classification and prediction algorithm called uRule for classifying uncertain...
We propose a probabilistic extension of Allen's interval algebra for managing uncertain temporal relations. Although previous work on various uncertain forms of quantitative and qualitative temporal networks have been proposed in the literature, little has been addressed to the most obvious type of uncertainty, namely the probabilistic one. More precisely, our model adapts the probabilistic constraint...
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