A Fast Stochastic Error-Descent Algorithm for Supervised Learning and Optimization

We address general optimization tasks that require finding a set of constant param(cid:173) eter values Pi that minimize a given error functional (p). For supervised learning, the error functional consists of some quantitative measure of the deviation between a desired state x T and the actual state of a network x, resulting from an input y and the parameters p. In such context the components of p consist of the con(cid:173) nection strengths, thresholds and other adjustable parameters in the network.