• Dynamic class imbalance learning for incremental LPSVM 

      Pang, Shaoning; Zhu, Lei; Chen, Gang; Sarrafzadeh, Hossein; Ban, Tao; Inoue, Daisuke (Neural Networks, 2013-02)
      Linear Proximal Support Vector Machines (LPSVMs), like decision trees, classic SVM, etc. are originally not equipped to handle drifting data streams that exhibit high and varying degrees of class imbalance. For online ...
    • Incremental and decremental max-flow for online semi-supervised learning 

      Zhu, Lei; Pang, Shaoning; Sarrafzadeh, Hossein; Ban, Tao; Inoue, Daisuke (Institute of Electrical and Electronics Engineers (IEEE), 2016-04-13)
      Max-flow has been adopted for semi-supervised data modelling, yet existing algorithms were derived only for the learning from static data. This paper proposes an online max-flow algorithm for the semi-supervised learning ...
    • Modelling land water composition scene for maritime traffic surveillance 

      Pang, Shaoning; Zhao, Jing.; Hartill, B.; Sarrafzadeh, Hossein (Inderscience Enterprises, 2016)
      Background modelling, used in many vision systems, must be robust to environmental change, yet sensitive enough to identify all moving objects of interest. Existing background modelling approaches have been developed to ...
    • Service provision control in federated service providing systems 

      Chen, Gang; Sarrafzadeh, Hossein; Pang, Shaoning (2013-03)
      Different from traditional P2P systems, individuals nodes of a Federated Service Providing (FSP) system play a more active role by offering a variety of domain-specific services. The service provision control (SPC) problem ...