Colloquia / Seminars / Industry Talks

Traditional supervised learning approaches require a lot of labeled training examples to achieve a strong generalization ability. In many machine learning and data mining applications, however, it is relatively easy to collect a large amount of unlabeled data yet rather expensive to get a large amount of labeled data since labeling the examples requires human effort. Semi-supervised learning attempts to exploit unlabeled data to help improve the learning performance. In this talk, I will briefly introduce some of our results in research of co-training style semi-supervised learning.

Zhi-Hua Zhou is currently Cheung Kong Professor and Head of the LAMDA group affiliated with both the Department of Computer Science & Technology and the National Key Laboratory for Novel Software Technology at Nanjing University. He has wide research interests, mainly including artificial intelligence, machine learning, data mining, information retrieval and pattern recognition. In these areas he has published over 50 papers in leading journals and conferences. He is on the editorial boards of journals including Artificial Intelligence in Medicine, Intelligent Data Analysis, Knowledge and Information Systems, etc., and served as guest editor/co-editor for journals including ACM/Springer Multimedia Systems, The Computer Journal, etc. He is/was a steering committee member of PAKDD, program committee chair/co-chair of PAKDD'07 and PRICAI’08, vice chair of IEEE ICDM'06, ICDM'08, etc., program committee member of many conferences including AAAI, ICML, ECML, ACM SIGKDD, IEEE ICDM, ACM Multimedia, etc., and general chair/co-chair or program committee chair/co-chair of over ten national conferences. He is the chair of the CAAI Machine Learning Society, vice chair of the CCF Artificial Intelligence & Pattern Recognition Society and chair of IEEE Computer Society Nanjing Chapter.