Implementation of the MLP Kernel

Abstract

This paper presents a MLP kernel. It maps all patterns in a class into a single point in the output layer space and maps different classes into different points. These widely separated class points can be used for further classifications. It is a layered feed-forward network. Each layer is trained using the class differences and trained independently layer after layer using a bottom-up construction. The class labels are not used in the training process. It can be used in separating multiple classes. © 2009 Springer Berlin Heidelberg.