Sentence examples for margin of separation from inspiring English sources

Briefly, based on the data labelled with two stimulus conditions, the SVM classifier defines a separating hyperplane from training samples by maximizing the margin of separation (i.e. the distance of the nearest sample to the separating hyperplane) between two conditions.

They find the linear separating hyperplane that maximizes the margin of separation in this higher dimensional space.

The goal of SVM is to find a linear optimal separating hyper plane so that the margin of separation between the two classes is maximized [24 27].

Support vector machine (SVM) [28,34] is a discriminative classification technique that operates by defining a decision boundary between two classes separated by a hyperplane that maximizes the margin of separation between the two classes.

The hyper plane was the best option to separate the data as it yields the maximum margin of separation between the classes.

Characterized as a machine learning algorithm capable of resolving linear and nonlinear classification problems, the main idea of classification by support vector is to separate examples with a linear decision surface and to maximize the margin of separation between the other training points [ 2].

Last month, Boeing came forward with a concept for safely directing planes in flight while increasing traffic by using smaller margins of separation.

TE is evaluated as an average measure of linear separation obtained by the removal of one sample per LOOCV round and by the subsequent SVM estimation of the margin of linear separation between the remaining samples.

Classification of patient samples with the rejection pattern is expressed as membership probability and quantified by the Euclidean distance of the data point to the maximal margin of a separation hyperplane between cases and controls in a multidimensional space constructed by the peptide classifiers (by the use of Support Vector Machines, SVM).

A range of membranes with differing molecular weight cutoffs (MWCO) between 10 50 kDa have been investigated to determine the greatest margin of metal-contaminant separation achievable.

The final SVM model employed the weighed-degree kernel (Sonnenburg et al., 2005), with soft margin constant C, which specifies the degree of separation between the two training classes of support vectors in the hyperplane was set to one.