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In this study, we used Support Vector Machines (SVM) for this type of classification task.
We used support vector machines to process these datasets.
In addition, we used support vector regression to develop a QM-based scoring function for the recognition of halogen bonds targeting methionine.
For modelling we used support vector machines [24], a machine learning method that has been used extensively in predictive modelling in cheminformatics [25, 26].
In this test we used support vector machine (SVM) with linear kernel implemented in the package libsvm and position-specific scoring matrix (PSSM) implemented according to the description in [7].
To determine the relative contribution of each influenza-related Twitter term, we used Support Vector Regression [14], an instance of the more general class of Support Vector Machines (SVM) [15], a supervised learning method generally applied to solve classification problems [16].
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The software we used supports adjustment for local dependencies through the introduction of direct effects [ 34- 36].
We used supported lipid bilayers (SLBs; Richter et al, 2006) as a platform for the construction of FG repeat-domain films (Fig 1A).
We use Support Vector Regression to estimate a mapping between the appearance of the eyes and the corresponding gaze direction.
Typically, we use Support Vector Machines (SVM) and Fuzzy C-means Clustering (FCM) to classify image pixels more efficiently.
We use support vector regression (SVR) to train the trend curves of three emotions (sadness, fear, and pleasure).
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