Character Recognition using Hierarchical Vector Quantization and Temporal Pooling

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Title Character Recognition using Hierarchical Vector Quantization and Temporal Pooling
Author Thornton, John Richard; Faichney, Jolon Bryce; Blumenstein, Michael Myer; Hine, Trevor John
Publication Title Lecture Notes in Artificial Intelligence
Editor Wayne Wobcke, Mengjie Zhang
Year Published 2008
Place of publication Heidelberg, Germany
Publisher Springer
Abstract In recent years, there has been a cross-fertilization of ideas between computational neuroscience models of the operation of the neo-cortex and artificial intelligence models of machine learning. Much of this work has focussed on the mammalian visual cortex, treating it as a hierarchically-structured pattern recognition machine that exploits statistical regularities in retinal input. It has further been proposed that the neocortex represents sensory information probabilistically, using some form of Bayesian inference to disambiguate noisy data. In the current paper, we focus on a particular model of the neocortex developed by Hawkins, known as hierarchical temporal memory (HTM). Our aim is to evaluate an important and recently implemented aspect of this model, namely its ability to represent temporal sequences of input within a hierarchically structured vector quantization algorithm. We test this temporal pooling feature of HTM on a benchmark of cursive hand-writing recognition problems and compare it to a current state-of-the-art support vector machine implementation. We also examine whether two pre-processing techniques can enhance the temporal pooling algorithm's performance. Our results show that a relatively simple temporal pooling approach can produce recognition rates that approach the current state-of-the-art without the need for extensive tuning of parameters. We also show that temporal pooling performance is surprisingly unaffected by the use of preprocessing techniques.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1007/978-3-540-89378-3_57
Copyright Statement Copyright 2008 Springer. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
ISBN 978-3-540-89377-6
Conference name 21st Australasian Joint Conference on Artificial Intelligence
Location Auckland, New Zealand
Date From 2008-12-03
Date To 2008-12-05
URI http://hdl.handle.net/10072/23558
Date Accessioned 2009-03-11
Language en_AU
Research Centre Behavioural Basis of Health; Griffith Health Institute; Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Pattern Recognition and Data Mining
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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