| 2008 |
|---|
| 14 | EE | Kai Zhang,
Ivor W. Tsang,
James T. Kwok:
Improved Nyström low-rank approximation and error analysis.
ICML 2008: 1232-1239 |
| 2007 |
|---|
| 13 | EE | Kai Zhang,
Ivor W. Tsang,
James T. Kwok:
Maximum margin clustering made practical.
ICML 2007: 1119-1126 |
| 12 | EE | Ivor W. Tsang,
András Kocsor,
James T. Kwok:
Simpler core vector machines with enclosing balls.
ICML 2007: 911-918 |
| 11 | EE | Ivor W. Tsang,
James T. Kwok:
Ensembles of Partially Trained SVMs with Multiplicative Updates.
IJCAI 2007: 1089-1094 |
| 10 | EE | Jooyoung Park,
Daesung Kang,
Jongho Kim,
James T. Kwok,
Ivor W. Tsang:
SVDD-Based Pattern Denoising.
Neural Computation 19(7): 1919-1938 (2007) |
| 2006 |
|---|
| 9 | EE | Ivor W. Tsang,
András Kocsor,
James T. Kwok:
Diversified SVM Ensembles for Large Data Sets.
ECML 2006: 792-800 |
| 8 | EE | Ivor W. Tsang,
James T. Kwok,
Shutao Li:
Learning the Kernel in Mahalanobis One-Class Support Vector Machines.
IJCNN 2006: 1169-1175 |
| 7 | EE | Ivor W. Tsang,
András Kocsor,
James T. Kwok:
Efficient kernel feature extraction for massive data sets.
KDD 2006: 724-729 |
| 6 | EE | Ivor W. Tsang,
James T. Kwok:
Large-Scale Sparsified Manifold Regularization.
NIPS 2006: 1401-1408 |
| 2005 |
|---|
| 5 | EE | Ivor W. Tsang,
James T. Kwok,
Kimo T. Lai:
Core Vector Regression for very large regression problems.
ICML 2005: 912-919 |
| 4 | EE | 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) |
| 2004 |
|---|
| 3 | EE | Ivor W. Tsang,
James T. Kwok:
Efficient Hyperkernel Learning Using Second-Order Cone Programming.
ECML 2004: 453-464 |
| 2003 |
|---|
| 2 | | James T. Kwok,
Ivor W. Tsang:
Learning with Idealized Kernels.
ICML 2003: 400-407 |
| 1 | | James T. Kwok,
Ivor W. Tsang:
The Pre-Image Problem in Kernel Methods.
ICML 2003: 408-415 |
