A Multi-layer ADaptive FUnction Neural Network (MADFUNN) for Letter Image Recognition

The letter image recognition dataset from UCI repository provides a complex pattern recognition problem which is to classify distorted raster images of English alphabetic characters. ADFUNN, the ANN deployed for this problem, is based on a linear piecewise neuron activation function that is modified by a novel gradient descent supervised learning algorithm. Linearly inseparable problems can be solved by ADFUNN, whereas the traditional single-layer perceptron (SLP) is incapable of solving them without a hidden layer. Multi-layer ADFUNNs (MADFUNNs) are used for the UCI distorted character recognition task. We construct a system with two parts, letter feature grouping and letter classification, to cope with the complexity of the wide diversity among the different fonts and attributes. Testing on 4,000 randomly selected test data, with all occurrences of the 16,000 training patterns removed, yields 87.6% (pure) generalisation. Allowing for naturally occurring instances of training data within the test data, yields 93.77% (natural) generalisation.