Iterative improvement of image classifiers using relaxation

A new objective function for neural net classifier design is presented, which has more free parameters than the classical objective function. An iterative minimization technique for the objective function is derived which requires the solution of multiple sets of numerically ill-conditioned linear equations. A numerically stable solution to the neural network design equations, which utilizes the conjugate gradient algorithm and a relaxation algorithm, is presented. The design method is applied to networks used to classify SAR imagery from remote sensing. The improvement of the iterative technique over classical design approaches is clearly demonstrated