The histopathology of childhood glaucoma is characterized by five different features.
EEG signals are digitally sampled and then transformed by three different feature extraction methods including nonparametric weighted feature extraction (NWFE), principal component analysis (PCA), and linear discriminant analysis (LDA), which were also used to reduce the feature dimension and project the measured EEG signals to a feature space spanned by their eigenvectors.
Based on these principles, we propose a novel method that generates saliency map with highlighted salient regions by utilizing two different features, namely visual saliency value and spatial weight.
In stage two, different features are classified by two SVM-based classifiers for color and monochrome classes, respectively.
In our experiment, the two different feature sets used to describe a sentence are word features and grammatical features (graph kernel extracted by Stanford Parser), respectively.
The high-level actions (explore, loop closure, reach goal) taken by the human are recorded along with three different features related to the uncertainty in the map.
Like mirCoS, RNAmicro uses twelve different features for SVM classification.
The present article investigates the free vibration and elastic stability behaviour of three-dimensional functionally graded sandwich beams featured by two different types of porosity, with arbitrary boundary conditions and resting on Winkler-Pasternak elastic foundations.
