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  • Robust Character Recognition Using a Hierarchical Bayesian Network

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    40684.pdf (119.2Kb)
    Author(s)
    Thornton, John
    Gustafsson, Torbjorn
    Blumenstein, Michael
    Hine, Trevor
    Griffith University Author(s)
    Thornton, John R.
    Blumenstein, Michael M.
    Hine, Trevor J.
    Gustafsson, Torbjorn EK.
    Year published
    2006
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    Abstract
    There is increasing evidence to suggest that the neocortex of the mammalian brain does not consist of a collection of specialised and dedicated cortical architectures, but instead possesses a fairly uniform, hierarchically organised structure. As Mountcastle has observed [1], this uniformity implies that the same general computational processes are performed across the entire neocortex, even though different regions are known to play different functional roles. Building on this evidence, Hawkins has proposed a top-down model of neocortical operation [2], taking it to be a kind of pattern recognition machine, storing ...
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    There is increasing evidence to suggest that the neocortex of the mammalian brain does not consist of a collection of specialised and dedicated cortical architectures, but instead possesses a fairly uniform, hierarchically organised structure. As Mountcastle has observed [1], this uniformity implies that the same general computational processes are performed across the entire neocortex, even though different regions are known to play different functional roles. Building on this evidence, Hawkins has proposed a top-down model of neocortical operation [2], taking it to be a kind of pattern recognition machine, storing invariant representations of neural input sequences in hierarchical memory structures that both predict sensory input and control behaviour. The first partial proof of concept of Hawkins' model was recently developed using a hierarchically organised Bayesian network that was tested on a simple pattern recognition problem [3]. In the current study we extend Hawkins' work by comparing the performance of a backpropagation neural network with our own implementation of a hierarchical Bayesian network in the well-studied domain of character recognition. The results show that even a simplistic implementation of Hawkins' model can produce recognition rates that exceed a standard neural network approach. Such results create a strong case for the further investigation and development of Hawkins' neocortically-inspired approach to building intelligent systems.
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    Conference Title
    AI 2006: Advances in Artificial Intelligence
    Publisher URI
    http://www.springer.com/east/home?SGWID=5-102-0-0-0&referer=www.springeronline.com
    http://www.comp.utas.edu.au/ai06/
    Copyright Statement
    © 2006 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
    Publication URI
    http://hdl.handle.net/10072/13103
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    • Conference outputs

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