Iteratively reweighted least squares based learning
Brad Warner, Manavendra Misra · 2002
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.