  Statistical modeling of nuclear data provides a novel approach to nuclear
systematics complementary to established theoretical and phenomenological
approaches based on quantum theory. Continuing previous studies in which global
statistical modeling is pursued within the general framework of machine
learning theory, we implement advances in training algorithms designed to
improved generalization, in application to the problem of reproducing and
predicting the halflives of nuclear ground states that decay 100% by the beta^-
mode. More specifically, fully-connected, multilayer feedforward artificial
neural network models are developed using the Levenberg-Marquardt optimization
algorithm together with Bayesian regularization and cross-validation. The
predictive performance of models emerging from extensive computer experiments
is compared with that of traditional microscopic and phenomenological models as
well as with the performance of other learning systems, including earlier
neural network models as well as the support vector machines recently applied
to the same problem. In discussing the results, emphasis is placed on
predictions for nuclei that are far from the stability line, and especially
those involved in the r-process nucleosynthesis. It is found that the new
statistical models can match or even surpass the predictive performance of
conventional models for beta-decay systematics and accordingly should provide a
valuable additional tool for exploring the expanding nuclear landscape.
