A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis

This paper introduces a learning method for two-layer feedforward neural networks based on sensitivity analysis, which uses a linear training algorithm for each of the two layers. First, random values are assigned to the outputs of the first layer; later, these initial values are updated based on sensitivity formulas, which use the weights in each of the layers; the process is repeated until convergence. Since these weights are learnt solving a linear system of equations, there is an important saving in computational time. The method also gives the local sensitivities of the least square errors with respect to input and output data, with no extra computational cost, because the necessary information becomes available without extra calculations. This method, called the Sensitivity-Based Linear Learning Method, can also be used to provide an initial set of weights, which significantly improves the behavior of other learning algorithms. The theoretical basis for the method is given and i...

• Type: Journal
• Year: 2006
• Where: JMLR
• Authors: Enrique Castillo, Bertha Guijarro-Berdiñas, Oscar Fontenla-Romero, Amparo Alonso-Betanzos
• Keywords: Feedforward Neural Networks, JMLR 2006, Learning Algorithms, Linear Training Algorithm