An Incremental Adaptive Network for On-Line Supervised Learning and Probability Estimation

In this paper, a novel hybrid utilisation of the Fuzzy ARTMAP (FAM) neural network and the Probabilistic Neural Network (PNN) is proposed for on-line learning and probability estimation tasks. There are two distinct advantages of the hybrid network. First, FAM is used as an underlying clustering algorithm to classify the input patterns into different recognition categories during the learning phase, resulting in a significant reduction of pattern nodes required in the PNN. Second, a non-parametric posterior probability distribution estimation procedure, in accordance with the PNN paradigm (i.e. the Parzen-window estimator), is employed during the prediction phase, where a probabilistic interpretation corresponding to the Bayes decision theorem can be provided for the predictions of FAM. In addition, several modifications are addressed to integrate both the networks effectively into a unified platform for enhancing generalisation..........