Nonlinear mapping plus linear classification

i.e.

mapping inseparable input space into a hidden space via some nonlinear mapping

and designing a linear classifier to classify the data in the hidden space

is one of the most popular approaches for designing a classifier for an inseparable classification problem.In fact

multi-layer perceptron (MLP)

support vector machine (SVM)

relevance vector machine (RVM) and radial basis function (RBF) are the examples of such approach.In this paper

we propose a novel classifier which is not based on this approach

but on stimulating pulse-coupled neurons such that the neurons belonging to a same class activate each other while those belonging to different classes inhibit each other according to the stimulus and the geometry of neurons in the input space.The result of the competition between neurons is that all the neurons belonging to one class will fire earlier than all the other neurons in the net

which is used for classification.A large number of experiments were conducted for classification of Iris data and simulated data where the overlap between patterns is very serious

as well as automatic target recognition for the model targets whose high-resolution range profiles were obtained from real microwave house.The experiments show that the proposed method is very effective in designing a classifier with good generalization ability and simple structure.