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    Learning polynomial neural networks of a near-optimal connectivity for detecting abnormal patterns in biometric data

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    Authors
    Nyah, Ndifreke
    Jakaite, Livija
    Schetinin, Vitaly
    Sant, Paul
    Aggoun, Amar
    Affiliation
    University of Bedfordshire
    Issue Date
    2016-09-01
    Subjects
    polynomial neural network
    biometrics
    evolution
    
    Metadata
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    Abstract
    Existing Machine Learning (ML) approaches known from the literature require the user to set and experimentally adjust parameters of a decision model to achieve the best result. When artificial neural networks (ANNs) are employed, a typical problem is setting of a proper network structure and learning parameters that are required to minimise possible overfitting. We propose a new evolutionary strategy of learning an ANN structure of a near-optimal connectivity from the given data and show that such structures are less prone to overfitting. The resultant ANN consists of a reasonably small number of neurons that are concisely described by a set of short-term polynomial functions of variables that make a distinct contribution to the output. The proposed technique has been tested on the ML benchmarks and the results showed that the performance is comparable with that obtained by the conventional ML methods that require ad hoc tuning.
    Citation
    Nyah N, Jakaite L, Schetinin V, Sant P, Aggoun A (2016) 'Learning polynomial neural networks of a near-optimal connectivity for detecting abnormal patterns in biometric data', SAI Computing Conference - London, Institute of Electrical and Electronics Engineers Inc..
    Publisher
    Institute of Electrical and Electronics Engineers Inc.
    URI
    http://hdl.handle.net/10547/624221
    DOI
    10.1109/SAI.2016.7556014
    Additional Links
    https://ieeexplore.ieee.org/document/7556014
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    9781467384605
    ae974a485f413a2113503eed53cd6c53
    10.1109/SAI.2016.7556014
    Scopus Count
    Collections
    Computing

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