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
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