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Online Supervised Learning for Digital Library

  • Ning Liu
  • Benyu Zhang
  • Jun Yan
  • Wensi Xi
  • Shuicheng Yan
  • Zheng Chen
  • Fengshan Bai
  • Wei-Ying Ma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3334)

Abstract

We propose an online learning algorithm for digital library. It learns from a data stream and overcomes the inherent problem of other incremental operations. Experiments on RCV1 show the superior performance of it.

References

  1. Weng, J., Zhang, Y., Hwang, W.-S.: Candid Covariance-free Incremental Principal Component Analysis. IEEE Trans. Pattern Analysis and Machine Intelligence (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Ning Liu
    • 1
  • Benyu Zhang
    • 2
  • Jun Yan
    • 3
  • Wensi Xi
    • 4
  • Shuicheng Yan
    • 2
  • Zheng Chen
    • 2
  • Fengshan Bai
    • 1
  • Wei-Ying Ma
    • 2
  1. 1.Department of MathematicsTsinghua UniversityBeijingP.R. China
  2. 2.Microsoft Research AsiaBeijingP.R. China
  3. 3.LMAM, School of Mathematical SciencesPeking UniversityBeijingP.R. China
  4. 4.Virginia Polytechnic Institute and State UniversityBlacksburgUSA

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