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Ivor W. Tsang: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Ivor W. Tsang, James T. Kwok
    Efficient Hyperkernel Learning Using Second-Order Cone Programming. [Citation Graph (0, 0)][DBLP]
    ECML, 2004, pp:453-464 [Conf]
  2. Ivor W. Tsang, András Kocsor, James T. Kwok
    Diversified SVM Ensembles for Large Data Sets. [Citation Graph (0, 0)][DBLP]
    ECML, 2006, pp:792-800 [Conf]
  3. James T. Kwok, Ivor W. Tsang
    Learning with Idealized Kernels. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:400-407 [Conf]
  4. James T. Kwok, Ivor W. Tsang
    The Pre-Image Problem in Kernel Methods. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:408-415 [Conf]
  5. Ivor W. Tsang, James T. Kwok, Kimo T. Lai
    Core Vector Regression for very large regression problems. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:912-919 [Conf]
  6. Ivor W. Tsang, James T. Kwok
    Ensembles of Partially Trained SVMs with Multiplicative Updates. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2007, pp:1089-1094 [Conf]
  7. Ivor W. Tsang, András Kocsor, James T. Kwok
    Efficient kernel feature extraction for massive data sets. [Citation Graph (0, 0)][DBLP]
    KDD, 2006, pp:724-729 [Conf]
  8. Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung
    Core Vector Machines: Fast SVM Training on Very Large Data Sets. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2005, v:6, n:, pp:363-392 [Journal]
  9. Ivor W. Tsang, András Kocsor, James T. Kwok
    Simpler core vector machines with enclosing balls. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:911-918 [Conf]
  10. Kai Zhang, Ivor W. Tsang, James T. Kwok
    Maximum margin clustering made practical. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:1119-1126 [Conf]
  11. Ivor W. Tsang, James T. Kwok
    Large-Scale Sparsified Manifold Regularization. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:1401-1408 [Conf]

  12. Improved Nyström low-rank approximation and error analysis. [Citation Graph (, )][DBLP]


  13. SimpleNPKL: simple non-parametric kernel learning. [Citation Graph (, )][DBLP]


  14. Domain adaptation from multiple sources via auxiliary classifiers. [Citation Graph (, )][DBLP]


  15. Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets. [Citation Graph (, )][DBLP]


  16. Domain Adaptation via Transfer Component Analysis. [Citation Graph (, )][DBLP]


  17. Spectral Embedded Clustering. [Citation Graph (, )][DBLP]


  18. Learning the Kernel in Mahalanobis One-Class Support Vector Machines. [Citation Graph (, )][DBLP]


  19. Extracting discriminative concepts for domain adaptation in text mining. [Citation Graph (, )][DBLP]


  20. Using large-scale web data to facilitate textual query based retrieval of consumer photos. [Citation Graph (, )][DBLP]


  21. T-IRS: textual query based image retrieval system for consumer photos. [Citation Graph (, )][DBLP]


  22. A Convex Method for Locating Regions of Interest with Multi-instance Learning. [Citation Graph (, )][DBLP]


  23. Predictive Distribution Matching SVM for Multi-domain Learning. [Citation Graph (, )][DBLP]


  24. A Semi-Supervised Framework for Feature Mapping and Multiclass Classification. [Citation Graph (, )][DBLP]


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