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Tutorials: Nov 13, 2011, Shanghai, China
Main Conference: Nov 14-17, 2011, Shanghai, China
Workshops: Nov 18, 2011, Hangzhou, China
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   Final program of ICONIP2011 and book of abstracts are available now.  
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Basics and Advances in Semi-supervised Learning


Irwin King
The Chinese University of Hong Kong, Hong Kong

Current on leave to be with AT&T Labs Research, San Francisco and UC Berkeley, Berkeley


The objective of this tutorial is to provide the audience an overview of recent developments in Semi-supervised learning. Semi-supervised learning has been used extensively in machine learning community for a variety of tasks including, but not limited to, classification, segmentation, clustering, etc. The tutorial plans to provide an introduction to the methods and approaches used and discusses about potential future developments in semi-supervised learning.

Content and Benefits:

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 other related algorithms, including self-training, co-training, EM-based methods, graph-based methods, and large-margin based methods. 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.

Target Audience:

This tutorial is for practitioners and researchers in the machine learning and data mining community who would like to know more about semi-supervised learning techniques and methods so that they can be integrated into their applications and/or projects.


       Dr. Irwin King's research interests include machine learning, social computing, web intelligence, data mining, and multimedia information processing. In these research areas, he has over 210 technical publications in journals and conferences. In addition, he has contributed over 20 book chapters and edited volumes. Moreover, Dr. King has over 30 research and applied grants. Notably, he is the Principal Investigator, Chief Technologist, and Co-Founder of the VeriGuide Project (a system that detects plagiarism to uphold academic quality).

       Dr. King is the Book Series Editor for “Social Media and Social Computing?with Taylor and Francis (CRC Press). He is also an Associate Editor of the IEEE Transactions on Neural Networks (TNN). 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). Moreover, he is also a Vice-President and Governing Board Member of APNNA. He also serves INNS as the Vice-President for Membership in the Board of Governors.

       Dr. King is Professor at the Department of Computer Science and Engineering, The Chinese University of Hong Kong (CUHK). 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. Currently, he is on leave from CUHK to be with AT&T Labs Research working on computational advertising projects and is also a visiting professor at the School of Information, University California at Berkeley.

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