A novel recursive partitioning criterion

Summary form only given, as follows. A data-driven algorithm for partitioning many-class classification problems has been developed. The algorithm generates tree-structured hybrid networks with controller nets at tree branches and local expert nets at the leaves. The controller nets recursively partition the feature space according to a novel misclassification minimization rule designed to create groupings of the classes which simplify the classification task. Each local expert is trained only dn a subset of the training data corresponding to one of the partitions. The advantage of this approach is that the classification task that each local expert performs is greatly simplified. This simplification helps to avoid the curse of dimensionality and scaling problems by allowing the local expert nets to focus their search for structure in a small portion of the input space