• DocumentCode
    3685424
  • Title

    A multi-fold string kernel for sequence classification

  • Author

    Aniruddha Maiti;Santanu Ghorai;Anirban Mukherjee

  • Author_Institution
    Aniruddha Maiti is with the Department of Computer and Information Sciences, Temple University, PA, USA
  • fYear
    2015
  • Firstpage
    6469
  • Lastpage
    6472
  • Abstract
    A novel framework is proposed to classify biological sequences using a kernel. It considers the topological information along with the primary structural information. The widely used string kernel for sequence classification does not take into account the structural information which might be available for biological sequences. The proposed kernels incorporate the additional structural information and thus make the features more informative.
  • Keywords
    "Kernel","Accuracy","Proteins","Support vector machines","Feature extraction","Hidden Markov models"
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE
  • ISSN
    1094-687X
  • Electronic_ISBN
    1558-4615
  • Type

    conf

  • DOI
    10.1109/EMBC.2015.7319874
  • Filename
    7319874
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3685424