Cluster-Weighted Modeling
It allows for data statistics and uncertainties to be derived from the model.
It allows for well known results from past praxis to be reused in a globally non-linear context, e.g. linear system’s theory or known synthesis techniques.
Local transparency compared to conventional Artificial Neural Networks.
Fast Convergence, giving reasonable prediction/classification results after the first iteration.
Non-linear function approximation through soft non-linear weighting of local generalized linear models: