Sentence examples for multiple hyperplanes were from inspiring English sources

In [19], multiple hyperplanes were used for learning to rank.

Multiple hyperplane was brought into play in [1, 24] for multiclass classification where each class was characterized by a set of hyperplanes.

The surface gradients of the hyperplanes are determined by pole assignment technique to guarantee the stability on the hyperplanes themselves.

Finally, the descent directions of supporting hyperplanes are employed for local enhancement to enhance exploitation capability.

First, the supporting hyperplanes are constructed for the neighboring individuals of the trial individual.

Hyperplanes are searched in the space of possible inputs; subsequently these hyperplanes are used to separate positive and negative patterns.

Many linear classifiers (also called hyperplanes) are able to separate the data.

To calculate the margin between data belonging to two different classes, two parallel hyperplanes are constructed, one on each side of the separating hyperplane, which are "pushed up against" the two data sets.

The distance separating the H1 and H−1 hyperplanes is (4) d = 2 ‖ w ‖ and it is called the margin.

The best hyperplane is the one that maximizes the distance (margin) of the two parallel hyperplanes defined in Equation 14.

The number of points on the hyperplane is.