Regulation of hierarchic decisions in intelligent systems

DETAILED DESCRIPTION This invention describes new methods for regulating hierarchic decisions in intelligent systems.
Storing additional statistics acquired during training for both terminal and non-terminal nodes alters a conventional decision tree structure.
These statistics are used to create smooth decision boundaries with the smoothness adjustable and optimizable through the choice of sample weights and regulation parameters.
Methods for trimming regulation trees and focusing them arise through selection of weights, tree depth, regulation parameters, and evaluation of accuracies obtained.
Methods for incrementally increasing the number of training samples for an already trained regulation decision tree provide graceful change to classification characteristics.
Methods for construction of compound trees support the incremental addition of new trained classes and new training samples.