Snap-drift learning for phrase recognition

This paper presents a new application of the snap-drift algorithm by S. W. Lee, et al. (2004): phrase recognition using a set of phrases from the Lancaster parsed corpus (LPC) by R. Garside, et al. (1987). The learning algorithm is the classifier version of snap-drift. In this version, along with the complementary concepts of fast minimalist learning (snap) and slow drift towards the input pattern, each node of the snap-drift neural network (SDNN) swaps between snap and drift modes when declining performance is indicated on that particular node. This method enables the SDNN to learn at node level, in the sense that each node has its learning mode toggled independently of the other nodes. Learning on each node is also reinforced by enabling learning with a probability that decreases with increasing performance. The simulations demonstrate that learning is stable, and the results have consistently shown similar classification performance and advantages in terms of speed in comparison with a multilayer perceptron (MLP) and back-propagation by J. Topper, et al. (2002), D. E. Rumelhart, et al. (1986) applied to the same problem.