Online supervised learning for digital library

Authors:
Ning Liu;Benyu Zhang;Jun Yan;Wensi Xi;Shuicheng Yan;Zheng Chen;Fengshan Bai;Wei-Ying Ma
Affiliations:
Department of Mathematics, Tsinghua University, Beijing, P.R. China;Microsoft Research Asia, Beijing, P.R. China;LMAM, School of Mathematical Sciences, Peking University, Beijing, P.R. China;Virginia Polytechnic Institute and State University, Blacksburg, VA;Microsoft Research Asia, Beijing, P.R. China;Microsoft Research Asia, Beijing, P.R. China;Department of Mathematics, Tsinghua University, Beijing, P.R. China;Microsoft Research Asia, Beijing, P.R. China
Venue:
ICADL'04 Proceedings of the 7th international Conference on Digital Libraries: international collaboration and cross-fertilization
Year:
2004

Citing 1
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Candid Covariance-Free Incremental Principal Component Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence

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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.