A Fast Stochastic Error-Descent Algorithm for Supervised Learning and Optimization
Gert Cauwenberghs · CaltechAUTHORS (California Institute of Technology) · 1992
A parallel stochastic algorithm is investigated for error-descent learning and optimization in deterministic networks of arbitrary topology. No explicit information about internal network structure is needed. The method is based on the model-free distributed learning mechanism of Dembo and Kailath. A modified parameter update rule is proposed by which each individual parameter vector perturbation contributes a decrease in error. A substantially faster learning speed is hence allowed. Furthermore, the modified algorithm supports learning time-varying features in dynamical networks. We analyze the convergence and scaling properties of the algorithm, and present simulation results for dynamic trajectory learning in recurrent networks. 1 Background and Motivation We address general optimization tasks that require finding a set of constant parameter values p i that minimize a given error functional E(p). For supervised learning, the error functional consists of some quantitativ...