Ivor Wai-Hung TSANG Assistant Professor School of Computer Engineering Nanyang Technological University Singapore 639798 Email: IvorTsang (at) ntu (dot) edu (dot) sg Ivor (dot) Tsang (at) gmail (dot) com

Call for Paper: International Workshop on Scalable Machine Learning and Applications (SMLA-10)

Some research positions in information extraction and machine learning are opening now. Anyone interested can send me a copy of your CV.

Dr Ivor Wai-Hung Tsang is currently an Assistant Professor in the School of Computer Engineering of Nanyang Technological University. He is currently also the Deputy Director of the Centre for Computational Intelligence of Nanyang Technological University. He received his Ph.D. degree in Computer Science from the Hong Kong University of Science and Technology (HKUST) in 2007. He was awarded the prestigious IEEE Transactions on Neural Networks Outstanding 2004 Paper Award in 2006. In 2009 he was conferred the second-class prize of Natural Science Award 2008, the Ministry of Education, China. 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.

Dr Tsang's scientific interests include machine learning, kernel methods and large scale optimization, and their applications to data mining and pattern recognitions. He has more than 40 research papers published in referred international journals and conference proceedings, including Journal of Machine Learning Research (JMLR), IEEE Transactions on Neural Networks (TNN), Neural Computation, IEEE Transactions on Audio, Speech and Language Processing (TASLP), Advances in Neural Information Processing Systems (NIPS), International Conference on Machine Learning (ICML), International Conference on Artificial Intelligence and Statistics (AISTATS), International Joint Conference on Artificial Intelligence (IJCAI), ACM SIGKDD Knowledge Discovery and Data Mining conference (KDD), etc.

Machine learning and Data mining:

Transfer learning, Domain adaptation and Distribution matching
Clustering, Semi-supervised learning and Multiple instance learning
Large-scale optimization and Combinatorial optimization
Support vector machines and Kernel methods
Boosting and Ensemble learning

Applications:

Internet vision and Multimedia retrieval
Computer vision and Object recognition
Image processing
Information extraction

PI, MOE AcRF Tier 1 - "Transferable Kernel Machines"

Yiming Liu, Dong Xu, Ivor W. Tsang, Jiebo Luo. Using Large-Scale Web Data to Facilitate Textual Query-based Retrieval of Consumer Photos. Proceedings of the ACM International Conference on Multimedia (ACM Multimedia 2009), Beijing, China, October 2009. (PDF) [Content track, Acceptance rate = 16%]

Brian Mak, Tsz-Chung Lai, Ivor W. Tsang, James T. Kwok. Maximum Penalized Likelihood Kernel Regression for Fast Adaptation. IEEE Transactions on Audio, Speech and Language Processing, 17(7): 1372-1381, September 2009. (PDF)
#The conference version of this paper was awarded with the Best Paper Award from the IEEE Hong Kong Chapter of Signal Processing Postgraduate Forum 2006

Lixin Duan, Ivor W. Tsang, Dong Xu, Tat-Seng Chua. Domain Adaptation from Multiple Sources via Auxiliary Classifiers. Proceedings of the 26th International Conference on Machine Learning (ICML 2009), Montreal, Quebec, June 2009. (PDF)

Bo Chen, Wai Lam, Ivor W. Tsang, Tak-Lam Wong. Extracting Discriminative Concepts for Domain Adaptation in Text Mining. Proceedings of the 15th ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2009), Paris, June 2009. (PDF)[Acceptance rate = 18%]

Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok and Qiang Yang. Domain Adaptation via Transfer Component Analysis. Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI 2009), Pasadena, CA, USA, July 2009.(PDF)(Project)

Lixin Duan, Ivor W. Tsang, Dong Xu, Stephen J. Maybank. Domain Transfer SVM for Video Concept Detection. Proceedings of the 21st IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami Beach, Florida, USA, June 2009.(PDF)
Domain Transfer SVM combines both Multiple Kernel Learning and Distribution Matching for Semi-Supervised Learning/Transduction

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

Yu-Feng Li, James T. Kwok, Ivor W. Tsang, Zhi-Hua Zhou. A Convex Method for Locating Regions of Interest with Multi-Instance Learning. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2009), pp.15-30, Bled, Slovenia, September 2009. (PDF)
The mixed integer program in Multiple Instance Learning can be solved by a scalable and efficient kernel learning algorithm

Feiping Nie, Dong Xu, Ivor W. Tsang, Changshui Zhang. Spectral Embedded Clustering. Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI 2009), Pasadena, CA, USA, July 2009. (PDF)(Proof)
The cluster assignment matrix can be showed as a low dimensional linear embedding of the data when the input dimension is high. The proposed SEC unifies Discriminative K-means, K-means and variants of Spectral Clustering

Yu-Feng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou. Tighter and Convex Maximum Margin Clustering. Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS) 2009, JMLR Workshop and Conference Proceedings Vol. 5, pp. 344-351, Clearwater Beach, Florida, USA, April 2009. (PDF)[Plenary Oral, Acceptance rate = 10%]
The mixed integer program in MMC can be solved by a scalable and efficient kernel learning algorithm, which achieves a tighter approximation than the SDP relaxation

Kai Zhang, Ivor W. Tsang, James T. Kwok. Maximum Margin Clustering Made Practical. IEEE Transactions on Neural Networks, 20(4): 583-596, April 2009. (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, 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)
RCA criterion is used to define a data-dependent kernel for semi-supervised learning

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)
Side information and Distance constraints are introduced to learn a kernel for semi-supervised learning

Jinfeng Zhuang, Ivor W. Tsang, Steven C. Hoi. SimpleNPKL: Simple Non-Parametric Kernel Learning. Proceedings of the 26th International Conference on Machine Learning (ICML 2009), Montreal, Quebec, June 2009. (PDF)
The Large-Scale SDP problem of Non-Parametric Kernel Learning with Pairwise Constraints can be solved by a very simple and scalable convex algorithm. A 5000-by-5000 non-parametric kernel matrix (25M variables) can be learned within a minute

Yu-Feng Li, James T. Kwok, Ivor W. Tsang, Zhi-Hua Zhou. A Convex Method for Locating Regions of Interest with Multi-Instance Learning. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2009), pp.15-30, Bled, Slovenia, September 2009. (PDF)
The mixed integer program in Multiple Instance Learning can be solved by a scalable and efficient kernel learning algorithm

Yu-Feng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou. Tighter and Convex Maximum Margin Clustering. Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS) 2009, JMLR Workshop and Conference Proceedings Vol. 5, pp. 344-351, Clearwater Beach, Florida, USA, April 2009. (PDF)[Plenary Oral, Acceptance rate = 10%]
The mixed integer program in MMC can be solved by a scalable and efficient kernel learning algorithm, which achieves a tighter approximation than the SDP relaxation

Kai Zhang, Ivor W. Tsang, James T. Kwok. Improved Nystrom low rank approximation and error analysis. Proceedings of the Twenty-Fifth International Conference on Machine Learning (ICML), pp.1232-1239, Helsinki, Finland. July 2008.(PDF) (Software)

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)

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)
SVM can be solved by the multi-scaled Enclosing Ball problem in Computational Geometry

Ivor W. Tsang, James T. Kwok. Large-scale sparsified manifold regularization. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2006. (PDF) [Plenary Oral, Acceptance rate = 3%]

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, 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)

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, 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)
SVM can be transformed equivalently as the Minimum Enclosing Ball problem in Computational Geometry. An approximation algorithm using core-set is firstly proposed in solving large-scale SVMs

Jinfeng Zhuang, Ivor W. Tsang, Steven C. Hoi. SimpleNPKL: Simple Non-Parametric Kernel Learning. Proceedings of the 26th International Conference on Machine Learning (ICML 2009), Montreal, Quebec, June 2009. (PDF)
The Large-Scale SDP problem of Non-Parametric Kernel Learning with Pairwise Constraints can be solved by a very simple and scalable convex algorithm. A 5000-by-5000 non-parametric kernel matrix (25M variables) can be learned within a minute

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

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)
RCA criterion is used to define a data-dependent kernel for semi-supervised learning

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)
Side information and Distance constraints are introduced to learn a kernel for semi-supervised learning

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)

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. (PDF)
SVM can be shown as a special case of AdaBoost Algorithm

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)

Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

IEEE Copyright Notice: © 200x IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE.

ACM Copyright Notice: Copyright © YYYY by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org. See ACM Copyright Policy for details.

SimpleNPKL: Simple Non-Parametric Kernel Learning. Special Lecture, Department of Computer Science, University of Alberta, Edmonton, Alberta, Canada, June 2009.

Large-Scale Maximum Margin Clustering. Invited Talk, DSO National laboratories, Singapore, April 2009.

Support Vector Machine Made Simpler. LAMDA Open Seminar, Department of Computer Science & Technology, Nanjing University, China, June 2008.

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

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

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

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

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

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

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

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

The NIPS 2009 Workshop on Transfer Learning for Structured Data (TLSD-09)

The 1st Asian Conference on Machine Learning (ACML 2009)
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)
The 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009)
The 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2010)

The 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009)
The 26th International Conference on Machine Learning (ICML 2009)
The 12th IEEE International Conference on Computer Vision (ICCV 2009)

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

Artificial Intelligence and Intelligent Systems

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