On The Universality of The Single-Layer Perceptron Model
Šarūnas Raudys · 2003
The Single Layer Perceptron (SLP) calculates a weighted sum of numerous inputs and produces output as a smooth non-linear two side bounded function of this sum. We show that this simple mathematical model, originally proposed to consider the information processing in brain cells, features much more universal principles. If appropriately trained, the SLP can implement many commonly known statistical classification and regression algorithms. The SLP training and retraining can be used to model aging processes in technology, biology and society. The SLP model can be utilized to simulate continuous and turbulent wave propagation in excitable medias. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.