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    Learning and development in Kohonen-style self organising maps.

    12818_Russell Keith-Magee 2001.pdf (6.886Mb)
    Access Status
    Open access
    Authors
    Keith-Magee, Russell
    Date
    2001
    Supervisor
    Professor Svetha Vankatesh
    Mike Robey
    Type
    Thesis
    Award
    PhD
    
    Metadata
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    School
    School of Computing
    URI
    http://hdl.handle.net/20.500.11937/162
    Collection
    • Curtin Theses
    Abstract

    This thesis presents a biologically inspired model of learning and development. This model decomposes the lifetime of a single learning system into a number of stages, analogous to the infant, juvenile, adolescent and adult stages of development in a biological system. This model is then applied to Kohonen's SOM algorithm.In order to better understand the operation of Kohonen's SOM algorithm, a theoretical analysis of self-organisation is performed. This analysis establishes the role played by lateral connections in organisation, and the significance of the Laplacian lateral connections common to many SOM architectures.This analysis of neighbourhood interactions is then used to develop three key variations on Kohonen's SOM algorithm. Firstly, a new scheme for parameter decay, known as Butterworth Step Decay, is presented. This decay scheme provides training times comparable to the best training times possible using traditional linear decay, but precludes the need for a priori knowledge of likely training times. In addition, this decay scheme allows Kohonen's SOM to learn in a continuous manner.Secondly, a method is presented for establishing core knowledge in the fundamental representation of a SOM. This technique is known as Syllabus Presentation. This technique involves using a selected training syllabus to reinforce knowledge known to be significant. A method for developing a training syllabus, known as Percept Masking, is also presented.Thirdly, a method is presented for preventing the loss of trained representations in a continuously learning SOM. This technique, known as Arbor Pruning, involves restricting the weight update process to prevent the loss of significant representations. This technique can be used if the data domain varies within a known set of dimensions. However, it cannot be used to control forgetfulness if dimensions are added to or removed from the data domain.

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