ANCHOR — A CONNECTIONIST ARCHITECTURE FOR PARTITIONING FEATURE SPACES AND HIERARCHICAL NESTING OF NEURAL NETS

We present a novel connectionist architecture for handling arbitrarily complex neural computations. The architecture — which we call an Artificial Neural Network Compiler for Hierarchical ORganisation (ANCHOR) — facilitates network hierarchy and the partitioning of the input space into a number of small, simpler sub-mappings. This strategy is a mix of both a modular approach for reduction of the problem complexity, and an ensemble-based approach for increasing prediction accuracy. ANCHOR is implemented around the concept of a Superneuron which is a generalised view of a neuron-processing element; a superneuron is a single (or higher-order) perceptron which can be trained individually with the support of multiple learning algorithms. The indistinguishability between a superneuron, and a neuron is employed in hierarchical nesting of superneurons, up to (theoretically) infinite depth, within other superneurons as well as linear or tree-structured cascading. Hierarchical decomposition of simple boolean functions has been demonstrated as proof-of-concept. It is argued that this strategy of data-partitioning for subsequent mapping onto a hierarchical classifier reduces the learning complexity and offers better generalisation.