Ivor Wai-Hung TSANG   Ph.D., Postdoc. Computer Science and Engineering Email: ivor (dot) tsang (at) gmail (dot) com ivor (at) cse (dot) ust (dot) hk

I have moved to Nanyang Technological University

Ivor Wai-Hung Tsang received his Ph.D. degree in Computer Science from the Hong Kong University of Science and Technology (HKUST) in 2007. He is currently a Postdoc at the Department of Computer Science and Engineering, HKUST. He was awarded the prestigious IEEE Transactions on Neural Networks Outstanding 2004 Paper Award in 2006. He was also awarded the Microsoft Fellowship in 2005, the Best Paper Award from the IEEE Hong Kong Chapter of Signal Processing Postgraduate Forum in 2006, and also the HKUST Honor Outstanding Student in 2001. He has been included in both Marquis Who's Who in Science and Engineering and Marquis Who's Who in Asia 2006-2007. His scientific interests include machine learning, kernel methods, large scale data mining and pattern recognition.

Machine learning and Data mining:

Support vector machines and Kernel methods
Boosting and Ensemble learning
Clustering, Semi-supervised learning and Multiple instance learning
Large-scale convex optimization and Approximation Algorithm
Graphical models and Structured prediction

Applications:

Pattern Recognition
Computer vision and Image processing
Information retrieval
Wireless sensor networks

Artificial Intelligence (seminar schedule)
Visgraph

Ivor W. Tsang, Andras Kocsor, James T. Kwok. Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines. IEEE Transactions on Neural Networks, 19(4): 610-624, April 2008. (PDF)

Jooyoung Park, Daesung Kang, Jongho Kim, James T. Kwok, Ivor W. Tsang. SVDD-Based Pattern De-Noising. Neural Computation, 19(7): 1919-1938, July 2007. (PDF)

James T. Kwok, Ivor W. Tsang, Jacek M. Zurada. A Class of Single-Class Minimax Probability Machines for Novelty Detection. IEEE Transactions on Neural Networks, 18(3): 778-785, May 2007. (PDF)

Ivor W. Tsang, James T. Kwok, Jacek M. Zurada. Generalized core vector machines. IEEE Transactions on Neural Networks, 17(5): 1126- 1140, Sept 2006. (PDF) (software)

Ivor W. Tsang, James T. Kwok. Efficient hyperkernel learning using second-order cone programming. IEEE Transactions on Neural Networks, 17(1):48- 58, Jan 2006. (PDF)

Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung. Core vector machines: Fast SVM training on very large data sets. Journal of Machine Learning Research, 6:363-392, 2005. (PDF) (software)

James T. Kwok, Ivor W. Tsang. The pre-image problem in kernel methods. IEEE Transactions on Neural Networks, 15(6):1517-1525, Nov 2004. (PDF) (software) #This paper was awarded with the IEEE Transactions on Neural Networks Outstanding 2004 Paper Award

Shutao Li, James T. Kwok, Ivor W. Tsang, Yaonan Wang. Fusing images with different focuses using support vector machines. IEEE Transactions on Neural Networks, 15(6):1555-1561, Nov 2004. (PDF)

James T. Kwok, Ivor W. Tsang. Linear dependency between epsilon and the input noise in epsilon-support vector regression. IEEE Transactions on Neural Networks,14( 3):544-553, May 2003. (PDF)

Kai Zhang, Ivor W. Tsang, James T. Kwok. Improved Nystrom low rank approximation and error analysis. To appear in the Twenty-Fifth International Conference on Machine Learning (ICML), Helsinki, Finland. July 2008.

Ivor W. Tsang, Andras Kocsor, James T. Kwok. Simpler core vector machines with enclosing balls. Proceedings of the Twenty-Fourth International Conference on Machine Learning (ICML), pp.911-918, Corvallis, Oregon, USA, June 2007.(PDF) (software)

Kai Zhang, Ivor W. Tsang, James T. Kwok. Maximum margin clustering made practical. Proceedings of the Twenty-Fourth International Conference on Machine Learning (ICML), pp.1119-1126, Corvallis, Oregon, USA, June 2007. (PDF) (software)

Ivor W. Tsang, James T. Kwok. Ensembles of Partially Trained SVMs with Multiplicative Updates. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp.1089-1094, Hyderabad, India, January 2007. (full paper)

Ivor W. Tsang, James T. Kwok. Large-scale sparsified manifold regularization. Proceedings of the Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2006. (PDF) (software in preparation)

Ivor W. Tsang, Andras Kocsor, James T. Kwok. Diversified SVM ensembles for large data sets. Proceedings of the European Conference on Machine Learning (ECML 2006), pp.792-800, Berlin, Germany, September 2006. (PDF)

Ivor W. Tsang, Andras Kocsor, James T. Kwok. Efficient kernel feature extraction for massive data sets. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06), pp.724-729, Philadelphia, USA, August 2006. (PDF)

Ivor W. Tsang, James T. Kwok, Shutao Li. Learning the kernel in Mahalanobis one-class support vector machines. Proceedings of the International Joint Conference on Neural Networks (IJCNN'06), pp.1169- 1175, Vancouver, Canada, July 2006. (PDF)

Ivor W. Tsang, James T. Kwok, Brian Mak, Kai Zhang, Jeffrey J. Pan. Fast speaker adaption via maximum penalized likelihood kernel regression. Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP'06), Toulouse, France, May 2006. (PDF) #This paper was awarded with the Best Paper Award from the IEEE Hong Kong Chapter of Signal Processing Postgraduate Forum 2006

Ivor W. Tsang, James T. Kwok. Very Large Scale Manifold Regularization using Core Vector Machines. Workshop on Large Scale Kernel Machines at Neural Information Processing Systems (NIPS 2005), Whistler, Canada, December 2005.

K.-F. Simon Wong, Ivor W. Tsang, Victor Cheung, S.-H. Gary Chan and James T. Kwok. Position Estimation for Wireless Sensor Networks. Proceedings of the IEEE Global Telecommunications Conference, (GLOBECOM 2005). St. Louis, USA, November, 2005. (PDF)

Ivor W. Tsang, James T. Kwok, Kimo T. Lai. Core Vector Regression for Very Large Regression Problems. Proceedings of the Twentieth-Second International Conference on Machine Learning (ICML-2005), pp.913-920, Bonn, Germany, August 2005.(PDF)(software)

Ivor W. Tsang, Pak-Ming Cheung, James T. Kwok. Kernel relevant component analysis for distance metric learning. Proceedings of the International Joint Conference on Neural Networks (IJCNN'05), pp.954-959, Montreal, Canada, July 2005.(PDF)(software)

Jooyoung Park, Daesung Kang, Jongho Kim, James T. Kwok, Ivor W. Tsang. Pattern de-noising based on support vector data description. Proceedings of the International Joint Conference on Neural Networks (IJCNN'05), pp.949-953, Montreal, Canada, July 2005. (PDF)

Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung. Very large SVM training using core vector machines. Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS 2005), Barbados, January 2005. (PDF)

Ivor W. Tsang, James T. Kwok. Efficient hyperkernel learning using second-order cone programming. Proceedings of the European Conference on Machine Learning (ECML 2004), pp.453-464, Pisa, Italy, September 2004. (PDF)

Calvin S. Chu, Ivor W. Tsang, James T. Kwok. Scaling up support vector data description by using core-sets. Proceedings of the International Joint Conference on Neural Networks (IJCNN 2004), pp.425-430, Budapest, Hungary, July 2004. (PDF)

James T. Kwok, Ivor W. Tsang. The pre-image problem in kernel methods. Proceedings of the International Conference on Machine Learning (ICML 2003), pp.408-415, Washington, D.C., USA, August 2003. (PDF) (software)

James T. Kwok, Ivor W. Tsang. Learning with idealized kernels. Proceedings of the International Conference on Machine Learning (ICML 2003), pp.400-407, Washington, D.C., USA, August 2003. (PDF)

Ivor W. Tsang, James T. Kwok. Distance metric learning with kernels. Proceedings of the International Conference on Artificial Neural Networks (ICANN 2003), pp.126-129, Istanbul, Turkey, June 2003. (PDF)

James T. Kwok, Ivor W. Tsang. Finding the pre-images in kernel principal component analysis. 6th Annual Workshop On Kernel Machines, NIPS 2002, Whistler, Canada, December 2002.

Machine learning on very large data sets. Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, September 2007.

Machine learning on very large data sets. School of Computing, National University of Singapore, Singapore, March 2007.

Kernel methods meet minimum enclosing balls. School of Computer Science, Simon Fraser University, Vancouver, Canada, December 2006.

Kernel methods meet minimum enclosing balls. Department Schoelkopf, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, September 2006.

Kernel methods meet minimum enclosing balls. Intelligent Data Analysis Group, Fraunhofer FIRST Institute, Berlin, Germany, September 2006.

Very large SVM training using core vector machines. School of Mathematics, Statistics and Computer Science, University of New England, Armidale, Australia, January 2005.

Efficient hyperkernel learning using second-order cone programming. School of Mathematics, Statistics and Computer Science, University of New England, Armidale, Australia, January 2005.

Very large SVM training using core vector machines. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia, January 2005.

Prof. Shueng-Han Gary Chan [projects on Wireless Sensor Networks]
Associate Professor, Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong

Prof. Andras Kocsor [projects on Kernel Methods, Large Scale Convex Optimization]
Senior Research Scientist, Deputy Head, Research Group on Artificial Intelligence, Department of Informatics, University of Szeged, Hungary

Prof. Brian Kan-Wing Mak [projects on Speech Recognition, Kernel Methods]
Associate Professor, Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong

Prof. Jooyoung Park [projects on Kernel Methods, Image processing]
Professor, Department of Control and Instrumentation, Engineering Korea University, Jochiwon, Korea

Prof. Jacek M. Zurada [projects on Novelty Detection, Large Scale Convex Optimization]
S. T. Fife Alumni Professor, IEEE Fellow, Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

The 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007)
The 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2008)

Journal of Machine Learning Research
IEEE Transactions on Neural Networks
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Systems, Man and Cybernetics (Part B)
Neurocomputing
Pattern Recognition

Comp 221: Fundamentals of Artificial Intelligence (Tutorial Notes)
Comp 271: Design and Analysis of Algorithms
Comp 151: Object-Oriented Programming
Comp 522: Machine Learning
Comp 300Y: Introduction to Machine Learning
Comp 103: Computer and Programming Fundamentals II
Comp 252: Principles of Systems Software

C/C++ Programming
Machine Learning Softwares
Machine Learning Journals
Machine Learning Benchmark Datasets
Useful Courses and Links in Machine Learning
Related Conferences in Machine Learning