  An ensemble method is introduced that utilizes randomization and loss
function gradients to compute a prediction. Multiple weakly-correlated
estimators approximate the gradient at randomly sampled points on the error
surface and are aggregated into a final solution. A scaling parameter is
described that controls a trade-off between ensemble correlation and precision.
Numerical methods for estimating optimal values of the parameter are described.
Empirical results are computed over a popular dataset. Inferential statistics
on these results show that the method is capable of outperforming existing
techniques in terms of increased accuracy.
