• DocumentCode
    1810540
  • Title

    Weighted least square ensemble networks

  • Author

    Chan, Lai-Wan

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong
  • Volume
    2
  • fYear
    1999
  • fDate
    36342
  • Firstpage
    1393
  • Abstract
    Ensemble of networks has been proven to give better prediction result than a single network. Two commonly used methods of determining the ensemble weights are simple average ensemble method and the generalized ensemble method. In the paper, we propose a weighted least square ensemble network. The major difference between this method and the other ensemble methods is that we do not assume that neither individual training data nor networks in the ensemble are independent and uncorrelated. Two variances of this model are also introduced, which require fewer computations. The sunspot data was used as a benchmark test of the proposed methods. From the result, we find that for the correlation ensemble, one variance of the weighted least square method gave the best ensemble weightings
  • Keywords
    learning (artificial intelligence); least squares approximations; neural nets; correlation ensemble; ensemble weights; least square ensemble networks; sunspot data; weighted least squares; Benchmark testing; Computer science; Equations; Least squares methods; Neural networks; Training data; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1999. IJCNN '99. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-5529-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.1999.831167
  • Filename
    831167