Sentence examples for most relevant classifiers from inspiring English sources

Indeed, dynamic selection and weighting allow to combine only the most relevant classifiers for each input probe.

Using feature selection, we determined the CAZy families from the six eSVMCAZy classifiers that are most relevant for identifying microbial cellulose-degraders.

A classifier is implemented to read the seismic registers at 30-second intervals, extract the most relevant features of each interval, and classify them into one of the three kinds of signals considered as most representative of this particular volcano.

The most relevant papers have been analyzed to point out which features and classifiers best work with depth data, if these fundamentals are specifically designed to process RGB-D images and, above all, how depth information can improve gesture recognition beyond the limit of standard approaches based on solely color images.

We define a filtering threshold n as the number of n most relevant high-level GO classes to the sentence determined by the classifiers.

A filtering threshold t was defined as the number of t most relevant high-level GO classes to the sentence determined by the classifiers.

Our approach consists firstly of generation of all possible combinations of most relevant values of the parameters included in construction of such classifier, or influencing its performance in anomaly detection, like feature selection, data normalization, architecture of neural network and activation function.

The proposed modifications (using only a half of the image, selecting the most relevant LBP features using Adaboost and bootstrapping at the stage of classifier training based on a soft margin SVM) result in forming of a classifier that is able to classify human face images by gender.

In each classifier, a binary PLS-regression was fit, with the most relevant genes associated with the subtype enrolled as predictive variables.

It turned out that loosing the feature NNNR yielded the worst classification performance (accuracy -0.89 compared to the classifier with all features) and therefore hinting for being the most relevant feature.

Rather than using all features to make a classifier we apply recursive feature elimination to select those that are most relevant.