Travelled to:1 × Canada
1 × China
1 × Finland
1 × France
1 × Germany
1 × Israel
1 × United Kingdom
3 × USA
Collaborated with:J.T.Kwok M.Tan K.Zhang A.Kocsor L.Duan D.Xu L.Wang J.Zhuang S.C.H.Hoi K.T.Lai Y.Zhai Y.Ong T.Chua B.Chen W.Lam T.Wong B.Vandereycken S.J.Pan Q.Xiang Q.Mao K.M.A.Chai H.L.Chieu Z.Zhao
Talks about:kernel (4) featur (4) learn (4) domain (3) adapt (3) veri (3) regress (2) problem (2) extract (2) cluster (2)
Person: Ivor W. Tsang
DBLP: Tsang:Ivor_W=
Contributed to:
2014201220102009
200920082007200620052003Wrote 15 papers:
- ICML-c2-2014-TanTWVP #matrix
- Riemannian Pursuit for Big Matrix Recovery (MT, IWT, LW, BV, SJP), pp. 1539–1547.
- ICML-2012-DuanXT #adaptation #learning
- Learning with Augmented Features for Heterogeneous Domain Adaptation (LD, DX, IWT), p. 89.
- ICML-2012-XiangMCCTZ #clustering #framework
- A Split-Merge Framework for Comparing Clusterings (QX, QM, KMAC, HLC, IWT, ZZ), p. 164.
- ICML-2012-ZhaiTTO
- Discovering Support and Affiliated Features from Very High Dimensions (YZ, MT, IWT, YSO), p. 226.
- ICML-2010-TanWT #dataset #feature model #learning
- Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets (MT, LW, IWT), pp. 1047–1054.
- ICML-2009-DuanTXC #adaptation #classification #multi
- Domain adaptation from multiple sources via auxiliary classifiers (LD, IWT, DX, TSC), pp. 289–296.
- ICML-2009-ZhuangTH #kernel #learning #named #parametricity
- SimpleNPKL: simple non-parametric kernel learning (JZ, IWT, SCHH), pp. 1273–1280.
- KDD-2009-ChenLTW #adaptation #concept #mining
- Extracting discriminative concepts for domain adaptation in text mining (BC, WL, IWT, TLW), pp. 179–188.
- ICML-2008-ZhangTK #analysis #approximate #fault #rank
- Improved Nyström low-rank approximation and error analysis (KZ, IWT, JTK), pp. 1232–1239.
- ICML-2007-TsangKK
- Simpler core vector machines with enclosing balls (IWT, AK, JTK), pp. 911–918.
- ICML-2007-ZhangTK #clustering
- Maximum margin clustering made practical (KZ, IWT, JTK), pp. 1119–1126.
- KDD-2006-TsangKK #feature model #kernel #performance #set
- Efficient kernel feature extraction for massive data sets (IWT, AK, JTK), pp. 724–729.
- ICML-2005-TsangKL #problem #scalability
- Core Vector Regression for very large regression problems (IWT, JTK, KTL), pp. 912–919.
- ICML-2003-KwokT #kernel #learning
- Learning with Idealized Kernels (JTK, IWT), pp. 400–407.
- ICML-2003-KwokT03a #kernel #problem
- The Pre-Image Problem in Kernel Methods (JTK, IWT), pp. 408–415.