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A reliable nonlinear quantitative structure activity relationship model was constructed by a support vector machine approach.
The local and global features are combined by a Support Vector Machine- (SVM-) based score level fusion strategy.
And then each column of the segmented rail image is represented by local frequency features and identified as corrugation line or not by a support vector machine (SVM).
In this paper, we evaluate several criteria rating feature sets obtained from various orthogonal filter banks for the classification by a Support Vector Machine.
A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector.
Finally, at text line grouping stage, the edges of the fully connected graph of the remaining character candidate regions are cut by a support vector machine classifier.
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Confidence in the spectral assignment is represented by an support vector machine (SVM) classifier (Trnka et al., 2014 ).
This paper is the first report on pattern recognition used for SELEX-based aptamer screening by applying a support vector classification (SVC) technique for a two-class problem.
The first approach presents a serial classifier fusion methodology by incorporating a support vector machine (SVM) classifier to diagnose PCA-based fault features.
By using a support vector machine with radial basis function, the fruit recognition algorithm was developed to detect and locate the apple in the trees automatically.
We generate a discriminative color model by training a support vector machine (SVM) with a radial basis function (RBF) kernel from the foreground and background colors.
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