ONLINE VERSUS OFF-LINE LEARNING FROM RANDOM EXAMPLES - GENERAL RESULTS

I propose a general model of on-line learning from random examples whi ch, when applied to a smooth realizable stochastic rule, yields the sa me asymptotic generalization error rate as optimal batch algorithms. T he approach is based on an iterative Gaussian approximation to the pos terior Gibbs distribution of rule parameters.