It then solve the problem by finding a linear hyperplane.
The GB uses a shared covariance matrix, so the separating margin between two classes is also a linear hyperplane.
Each weak classifier is a linear hyperplane in an 11D feature space composed of R,G,B color and a histogram of gradient orientations.
For the latter, we focused on support vector machines (SVM), which seek a linear hyperplane that separates observations from two (or more) distinct classes.
Each class is represented by a linear element, which implements a hyperplane in the feature space.
However, if the value of T is fixed, it is possible to solve the optimization problem via majorization theory in a specific hyperplane described by a linear equation ∑ j = 1 M x j = T.
SVM was first developed by Vapnik [ 39] and was originally employed to find a linear separating hyperplane that maximizes the distance between two classes.
To address the difficulty of seeking a linear discriminant hyperplane in the tensor space, TLMMC utilizes a set of local multilinear hyperplanes to construct a global nonlinear one.
In a simple pattern recognition problem, SVM uses a linear separating hyperplane to create a classifier with a maximal margin.
