2014
DOI: 10.1007/s13202-014-0128-8
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Abstract: Accurate determination of oil bubble point pressure (Pb) from laboratory experiments is time, cost and labor intensive. Therefore, the quest for an accurate, fast, and cheap method of determining Pb is inevitable. Since support vector based regression satisfies all components of such a quest through a supervised learning algorithm plant based on statistical learning theory, it was employed to formulate available PVT data into Pb. Open-sources literature data were used for SVR model construction and Iranian Oil… Show more

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Cited by 7 publications
(2 citation statements)
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“…In this study, SVR is employed to predict GWQI. In order to improve the forecasting capability of the model, the primary goal of SVR is to simultaneously minimize the system complication and prediction error (Bagheripour et al 2015). SVR is a supervised classifier that can quickly and accurately fit and predict samples.…”
Section: Support Vector Regression (Svr)mentioning
confidence: 99%
“…In this study, SVR is employed to predict GWQI. In order to improve the forecasting capability of the model, the primary goal of SVR is to simultaneously minimize the system complication and prediction error (Bagheripour et al 2015). SVR is a supervised classifier that can quickly and accurately fit and predict samples.…”
Section: Support Vector Regression (Svr)mentioning
confidence: 99%
“…The estimation approach in this function is a refinement of a support vector machine to solve knotty regression tasks. Recently, this method has attracted much attention because it showed significant ability in solving diverse regression problems with excellent accuracy. In comparison to neural networks, this technique is superior in generalization domains which originated from how it minimizes the risk. In this regard, support vector regression applies the structural risk minimization principle to reduce the upper bound on expected risk, while neural networks use the empirical risk minimization principle to decrease the error on the training data .…”
Section: Model Descriptionmentioning
confidence: 99%