  Given a {features, target} dataset, we introduce an incremental algorithm
that constructs an aggregate regressor, using an ensemble of neural networks.
It is well known that ensemble methods suffer from the multicollinearity issue,
which is the manifestation of redundancy arising mainly due to the common
training-dataset. In the present incremental approach, at each stage we
optimally blend the aggregate regressor with a newly trained neural network
under a convexity constraint which, if necessary, induces negative
correlations. Under this framework, collinearity issues do not arise at all,
rendering so the method both accurate and robust.
