Semi-supervised Methods

June 25, 2009

Irwin King

Department of Computer Science and Engineering
The Chinese University of Hong Kong

  • [June 25, 2009, misc | www]
    Irwin King, Semi-supervised Methods, Seminar at the Academia Sinica,Taipei, Taiwan: , June 25, 2009.


Semi-supervised learning has attracted much attention recently in both the research and application areas. Semi-supervised learning is a family of machine learning techniques, which make use of both labeled and unlabeled data for training. In this talk, I will first introduce the basic concepts in semi-supervised learning. Specially, I will elaborate the use of Semi-supervised SVM as the classifier for semi-supervised learning. Then I will focus on kernel selection and feature selection for semi-supervised learning. I will introduce three recently developed models for semi-supervised learning developed by our group. The first one is an efficient convex relaxation method for Transductive SVM. It improves the computational efficiency of previous convex relaxation methods. Then, I will introduce a level method for learning an optimal kernel for semi-supervised learning based on manifold regularization. After that, I will present a method for semi-supervised feature selection, which selects features through manifold regularization with the aid of unlabeled data.

Brief Profile

Dr. King's research interests include machine learning, web intelligence & social computing, and multimedia processing. In these research areas, he has over 150 technical publications in journals (JMLR, ACM TOIS, IEEE TNN, IEEE BME, PR, IEEE SMC, JAMC, JASIST, IJPRAI, NN, etc.) and conferences (NIPS, CIKM, SIGIR, IJCNN, ICONIP, ICDAR, WWW, 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, which detects similar sentences and performs readability analysis of text-based documents in both English and in Chinese to promote academic integrity and honesty.

Dr. 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).

Dr. King joined the Chinese University of Hong Kong in 1993. He received his B.Sc. degree in Engineering and Applied Science from California Institute of Technology, Pasadena and his M.Sc. and Ph.D. degree in Computer Science from the University of Southern California, Los Angeles.

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