LAUSR

Static pattern recognition requires a machine to classify an object on the basis of a combination of attributes and is typically performed using machine learning techniques such as support vector machines and multilayer perceptrons. Unusually, in this study, we applied a successful time-series processing neural network architecture, the echo state network (ESN), to a static pattern recognition task. The networks were presented with clamped input data patterns, but in this work, they were allowed to run until their output units delivered a stable set of output activations, in a similar fashion to previous work that focused on the behaviour of ESN reservoir units. Our aim was to see if the short-term memory developed by the reservoir and the clamped inputs could deliver improved overall classification accuracy. The study utilized a challenging, high dimensional, real-world plant species spectroradiometry classification dataset with the objective of accurately detecting one of the world’s top 100 invasive plant species. Surprisingly, the ESNs performed equally well with both unsettled and settled reservoirs. Delivering a classification accuracy of 96.60%, the clamped ESNs outperformed three widely used machine learning techniques, namely support vector machines, extreme learning machines and multilayer perceptrons. Contrary to past work, where inputs were clamped until reservoir stabilization, it was found that it was possible to obtain similar classification accuracy (96.49%) by clamping the input patterns for just two repeats. The chief contribution of this work is that a recurrent architecture can get good classification accuracy, even while the reservoir is still in an unstable state.