Sentence examples for by an support vector from inspiring English sources

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Confidence in the spectral assignment is represented by an support vector machine (SVM) classifier (Trnka et al., 2014 ).

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A reliable nonlinear quantitative structure activity relationship model was constructed by a support vector machine approach.

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).

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.

In this paper, we evaluate several criteria rating feature sets obtained from various orthogonal filter banks for the classification by a Support Vector Machine.

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.

Feature extraction is defined by a subsequent reduction of the selected voxels using principal component analysis (PCA) or partial least squares (PLS) techniques while classification is performed by a support vector machine (SVM).

Data from genome‐wide studies can be individually retrieved and are also processed by a support vector machine (SVM) to generate a score that indicates the likelihood of a candidate protein having a mitochondrial location.

In this system, three kinds of feature sets, wavelet packet transform (WPT) coefficients, stationary wavelet transform (SWT) coefficients, and the time and frequency domain hybrid (TFDH) features, are adopted and processed by a support vector machine (SVM) classifier.

Two color representations, viz., local color moments in Ohta space and the HSV color histogram, are attempted to characterize HCN frames, which are isolated by a support vector machine (SVM) classifier in Step-1.

In this article, the complex left ventricle (LV) shape and motion of 50 RT3datasetsets are represented by novel 4-D descriptors — 4-D sphericity, volume and shape, from which novel indices were derived by principal component analysis (PCA) and subsequently analyzed by a support vector machine (SVM) classifier to assess their capability of LVMD characterization and CRT outcome prediction.

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