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%]
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
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
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 Domain Adaptation and Multiple Kernel Learning
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. 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
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. (PDF)
SVM can be shown as a special case of AdaBoost Algorithm
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, 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, 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
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)