Title :
Iteratively reweighted least squares based learning
Author :
Warner, Bradley A. ; Misra, Manavendra
Author_Institution :
Dept. of Math. Sci., US Air Force Acad., Colorado Springs, CO, USA
Abstract :
We demonstrate a method to obtain maximum likelihood weight estimates for a multi-layered feedforward neural network using least squares. The proposed method uses the Fisher´s information matrix instead of the Hessian matrix to compute the search direction. Since this matrix is formulated as an inner product, it is guaranteed to be positive definite. The formulation used by the method also provides an interesting way of highlighting the multicollinearity problem in multilayered feedforward networks
Keywords :
feedforward neural nets; iterative methods; learning (artificial intelligence); least squares approximations; matrix algebra; maximum likelihood estimation; multilayer perceptrons; Fisher´s information matrix; inner product; iteratively reweighted least squares based learning; maximum likelihood weight estimates; multi-layered feedforward neural network; multicollinearity problem; positive definite matrix; search direction; Artificial neural networks; Convergence; Feedforward neural networks; Feedforward systems; Least squares approximation; Least squares methods; Maximum likelihood estimation; Military computing; Multi-layer neural network; Neural networks;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.685967
