Patent ID: 6941289
Filing Date: 2005-09-06
Classification: G06N

Abstract:
1. A computer-implemented method for building an artificial neural network from a set of different types of candidate activation functions, comprising the steps of: retrieving an input data set that includes observations and at least one target for the observations; reducing the input data set such that the reduced input data set contains a number of points less than the number of observations; optimizing parameters of the candidate activation functions with respect to the reduced input data set through use of an objective function; generating results for each of the candidate activation functions using the optimized parameters of the candidate activation functions and the reduced input data set; selecting a first activation function from the candidate activation functions based upon the generated results; using the selected first activation function within a first layer of the artificial neural network, wherein residuals result from predictions by the first layer's selected activation function of the target; and selecting a second activation function different from the first activation function to form a second layer based upon the second activation function's capability to predict the residuals.