Modal Learning in a Neural Network

Conference paper

Lee, S. and Palmer-Brown, Dominic 2006. Modal Learning in a Neural Network. Proceedings of the AC&T, pp. 42-47
AuthorsLee, S. and Palmer-Brown, Dominic
TypeConference paper
Abstract

This paper presents an application of the snap-drift modal learning algorithm developed in
recent years by Lee and Palmer-Brown (Lee, 2004a). The application involves phrase recognition
using a set of phrases from the Lancaster Parsed Corpus (LPC) (Garside, 1987). The learning
algorithm is the classifier version of snap-drift. The twin modes of minimalist learning (snap) and
slow drift towards the input pattern are applied alternately. Each neuron of the Snap-Drift Neural
Network (SDNN) swaps between snap and drift modes when declining performance is indicated on
that particular node, so 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 neural networks applied to the same
problem.

Keywordssnap-drift; Snap-Drift Neural Network (SDNN); Multilayer Perceptron (MLP); learning algorithm
Year2006
ConferenceProceedings of the AC&T, pp
Publisher's version
License
CC BY-ND
Publication dates
Print2006
Publication process dates
Deposited09 Jun 2010
Web address (URL)http://www.uel.ac.uk/act/proceedings/documents/ACT06Proceeding.pdf
http://hdl.handle.net/10552/792
Additional information

Citation:
Lee, S.W; Palmer-Brown, D. (2006) ‘Modal Learning in a Neural Network’ Proceedings of the AC&T, pp.42-47..

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