Enhancing incremental learning in MLP networks using ensemble encoding of network inputs

Local learning techniques associated with multilayer perceptron (MLP) networks typically employ receptive fields as an integral part of the network. However, data representation schemes that employ multiple, overlapping receptive fields to preprocess network inputs can be another source of local learning in MLP networks. Earlier work has shown that ensemble encoding, a distributed data representation scheme, promotes local learning and can accelerate learning in MLP networks. We demonstrate that networks using ensemble encoding display an enhanced capacity for incremental learning