Linearizing layers of radial binary classifiers with movable centers

Ranked layers of binary classifiers are used for the linearization of learning sets composed of multivariate feature vectors. After transformation by ranked layer, each learning set can be separated by a hyperplane from the sum of other learning sets. Ranked layers can be designed, among others, from radial binary classifiers. This work elaborates on designing ranked layers from radial binary classifiers with movable centers.