WCCI2010 (IJCNN2010) Conference Tutorial

T25: Basics and Advances in Semi-supervised Learning

Irwin King(1) and Zenglin Xu (2)

(1) Department of Computer Science and Engineering
The Chinese University of Hong Kong

(2) Cluster of Excellence: MMCI
Saarland University & MPI Informatics
Saarbruecken, 66123 Germany

  • [2010, misc | www]
    Irwin King and Zenglin Xu, {Basic and Advances on Semi-supervised Learning, Tutorial, WCCI2010 (IJCNN2010), Barcelona, Spain}, 2010.


Semi-supervised learning is an active topic in both the research and application fields of data mining. In many applications, labeled data are usually expensive to obtain and unlabeled data are widely observed. Semi-supervised learning is important in that the unlabeled data can help to improve the performance of supervised learning and thus greatly reduces the human effort in labeling data. In this tutorial, we will first introduce the fundamental assumptions in semi-supervised learning. Based on these assumptions, we will introduce the related algorithms, including self-training, co-training, EM-based methods, graph-based methods, and large-margin based methods. To better understand these algorithms, we will show the demos. Furthermore, we will also introduce some applications of these algorithms. In particular, we will present a study of these semi-supervised learning algorithms in privacy preservation in social network analysis. Finally, we will review recent advances and future perspectives in semi-supervised learning.

Brief Profile

Irwin King's research interests include machine learning, web intelligence & social computing, and multimedia processing. In these research areas, he has over 200 technical publications in journals (JMLR, ACM TOIS, IEEE TNN, Neurocomputing, NN, IEEE BME, PR, IEEE SMC, JAMC, JASIST, IJPRAI, DSS, etc.) and conferences (NIPS, IJCAI, CIKM, SIGIR, KDD, PAKDD, ICDM, WWW, WI/IAT, WCCI, IJCNN, ICONIP, ICDAR, etc.). In addition, he has contributed over 20 book chapters and edited volumes. Moreover, Dr. King has over 30 research and applied grants. One notable system he has developed is the VeriGuide System, previously known as the CUPIDE (Chinese University Plagiarism IDentification Engine) system, which detects similar sentences and performs readability analysis of text-based documents in both English and in Chinese to promote academic integrity and honesty.

Irwin King is an Associate Editor of the IEEE Transactions on Neural Networks (TNN) and IEEE Computational Intelligence Magazine (CIM). He is a member of the Editorial Board of the Open Information Systems Journal, Journal of Nonlinear Analysis and Applied Mathematics, and Neural Information Processing–Letters and Reviews Journal (NIP-LR). He has also served as Special Issue Guest Editor for Neurocomputing, International Journal of Intelligent Computing and Cybernetics (IJICC), Journal of Intelligent Information Systems (JIIS), and International Journal of Computational Intelligent Research (IJCIR). He is a senior member of IEEE and a member of ACM, International Neural Network Society (INNS), and Asian Pacific Neural Network Assembly (APNNA). Currently, he is serving the Neural Network Technical Committee (NNTC) and the Data Mining Technical Committee under the IEEE Computational Intelligence Society (formerly the IEEE Neural Network Society). He is also a Vice-President and Governing Board Member of the Asian Pacific Neural Network Assembly (APNNA).

Presentation Materials

  • [2010, misc | www]
    Irwin King and Zenglin Xu, {Basic and Advances on Semi-supervised Learning, Tutorial, WCCI2010 (IJCNN2010), Barcelona, Spain}, 2010.
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