Sentence examples for machine classifier to from inspiring English sources

We test imagery chain standardization by using support vector machine classifier to classify the land cover of the states of Louisiana and Arkansas, USA, into agriculture, barren lands, forest, urban, water, and wetlands.

This study reports the development of a support vector machine classifier to aid in the design of membrane active peptides.

Then SVM-RFE algorithm is applied for feature selection and reduced vectors are input to a support vector machine classifier to predict subcellular location of apoptosis proteins.

These feature representation vectors of convolutional layers and the output feature vector of the global pooling layer are normalized and cascaded as a whole feature vector, which is finally utilized to train Support Vector Machine classifier to obtain the recognition model.

Finally, the optimal feature set was input into support vector machine classifier to establish the final prediction model.

These clusters are then aggregated by a support-vector machine classifier to a final absolute dense area, D A, segmentation (Fig.  1d).

This article proposes an effort to apply the multi-class support vector machine classifiers to classify the supraspinatus image into different disease groups that are normal, tendon inflammation, calcific tendonitis and supraspinatus tear.

Boella et al. used the SVM (Support Vector Machines) classifier to classify 233 legal documents into six different areas of tax law [31].

The article presents an easy to implement approach for indoor localization and navigation that combines Bayesian filtering with support vector machine classifiers to associate high-dimensionality cellular telephone network received signal strength fingerprints to distinct spatial regions.

The low-frequency part of the decomposed data was successfully used for PCA analysis and to train support vector machine classifiers to distinguish between irradiated and non-irradiated samples of S. cerevisiae strains.

Finally, the selected features are forwarded to a least square support vector machine (LS_SVM) classifier to classify the EEG signals.