Learning performance measures for MLP networks

Training of MLP networks is mainly based on implementation of first order line search optimization techniques. Determination of the search direction is given by an error matrix for a neural network. The error matrix contains essential information not only about the search direction, but also about the specific features of the error landscape. The analysis of the error matrix based on the estimation of its spectral radius provides relative measure on proportion of the algorithm´s movement in multidimensional weight/error space. Furthermore, the estimate of a spectral radius forms a suitable reference ground for derivation of performance measures. The article presents effective and computationally inexpensive performance measures for MLP networks. Such measures allow not only monitoring of networks performance but On their basis an individual performance measure for each structural element can be derived. This has direct applicability to the pruning strategies of MLP networks. In addition, the proposed performance measures permit detection of specific shapes of error surfaces such as flat regions and sharp slopes. This feature is of essential importance for algorithms implementing dynamic modifications of learning rate