Sentence examples for maximal margin hyperplane from inspiring English sources

The underlying idea behind the SVM is to calculate a maximal margin hyperplane that performs a binary classification of the data [22].

The underlying idea of SVM classifiers is to calculate a maximal margin hyperplane separating the cases and controls.

Classification is performed by determining the Euclidian distance of the data set to the n-1 dimensional maximal margin hyperplane (absolute value of the normal vector) and the direction of the vector (class 1 or class 2).

Classification is performed by determining the Euclidian distance (defined as the SVM score) of the polypeptides to the (n-1) dimensional maximal margin hyperplane and the direction of the vector.

SVM [ 13] separates a set of binary-labeled training data by means of a maximal margin hyperplane, building a decision function R N → ± 1.

SVM was introduced by [ 51] aiming to find the Maximal Margin Hyperplane (MMH) based on the concept of the support vector theory to minimize the error.

Selection of kernel function parameter is also important to define the maximum margin hyperplane.

Unlike the traditional SVM that builds a maximum margin hyperplane in the original high-dimensional space where n ~ 10 – 10, MICA-SVM separates biological samples by constructing the maximum margin hyperplane in the spanned subspace where using the meta-samples.

The separating hyperplane is determined by a) mapping the input space into a higher dimensional feature space through a kernel function, and b) constructing in this feature space two maximal margin hyperplanes [ 16] to separate the mapped data samples in the higher dimensional space.

Consequently, the maximum margin hyperplane fits in a feature space with the help of the nonlinear SVM.

Their power comes from the combination of the kernel trick and maximum margin hyperplane separation.