Blar i AURA på forfatter "Hou, Jian"

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    • A fast Logdet divergence based metric learning algorithm for large data sets classification 

      Mei, Jiangyuan; Hou, Jian; Chen, Jicheng; Karimi, Hamid Reza (Journal article; Peer reviewed, 2014)
      Large data sets classification is widely used in many industrial applications. It is a challenging task to classify large data sets efficiently, accurately, and robustly, as large data sets always contain numerous instances ...

    • A novel data-driven fault diagnosis algorithm using multivariate dynamic time warping measure 

      Mei, Jiangyuan; Hou, Jian; Karimi, Hamid Reza; Huang, Jiarao (Journal article; Peer reviewed, 2014)
      Process monitoring and fault diagnosis (PM-FD) has been an active research field since it plays important roles in many industrial applications. In this paper, we present a novel data-driven fault diagnosis algorithm which ...

    • Exploring the best classification from average feature combination 

      Hou, Jian; Liu, Wei-Xue; Karimi, Hamid Reza (Peer reviewed; Journal article, 2014)
      Feature combination is a powerful approach to improve object classification performance. While various combination algorithms have been proposed, average combination is almost always selected as the baseline algorithm to ...

    • Research on vocabulary sizes and codebook universality 

      Liu, Wei-Xue; Hou, Jian; Karimi, Hamid Reza (Journal article; Peer reviewed, 2014)
      Codebook is an effective image representation method. By clustering in local image descriptors, a codebook is shown to be a distinctive image feature and widely applied in object classification. In almost all existing works ...

    • Sampling based average classifier fusion 

      Hou, Jian; Liu, Wei-Xue; Karimi, Hamid Reza (Journal article; Peer reviewed, 2014)
      Classifier fusion is used to combine multiple classification decisions and improve classification performance. While various classifier fusion algorithms have been proposed in literature, average fusion is almost always ...