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    A Study of Hierarchical Concatenation Networks in the Area of Pattern Recognition

    Ramli I 2018.pdf (3.410Mb)
    Access Status
    Open access
    Authors
    Ramli, Irwan
    Date
    2018
    Supervisor
    Cesar Ortega-Sanchez
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Science and Engineering
    School
    Department of Electrical & Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/73517
    Collection
    • Curtin Theses
    Abstract

    Hierarchical Concatenation Networks (HCN) are inspired by the way humans recognize patterns; i.e. by concatenating small features. In HCNs patterns are split into small parts, and then concatenated and activated in the network’s layers. The research in this thesis investigated and explored feature extraction methods, similarity measures, and classification using HCNs. Results indicate that HCNs can be used in automatic pattern recognition systems with better performance rate on the lower layer than the top layer.

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