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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3334))

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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.

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References

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

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© 2004 Springer-Verlag Berlin Heidelberg

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Liu, N. et al. (2004). Online Supervised Learning for Digital Library. In: Chen, Z., Chen, H., Miao, Q., Fu, Y., Fox, E., Lim, Ep. (eds) Digital Libraries: International Collaboration and Cross-Fertilization. ICADL 2004. Lecture Notes in Computer Science, vol 3334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30544-6_108

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  • DOI: https://doi.org/10.1007/978-3-540-30544-6_108

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24030-3

  • Online ISBN: 978-3-540-30544-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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