BibSLEIGH — James_T

Travelled to:

1 × Finland
1 × Germany
1 × Israel
1 × Spain
2 × China
3 × Canada
5 × USA

Collaborated with:

I.W.Tsang K.Zhang A.Kocsor Z.Zhang D.Yeung R.Zhang W.Bi W.Zhong P.Cheung M.H.C.Law M.Li B.Lu Y.Li Z.Zhou B.Parvin K.T.Lai H.Zhu L.Qu J.Dai S.Yan X.Tang S.Li J.Peng J.Zhang

Talks about:

learn (6) kernel (5) regress (3) vector (3) model (3) larg (3) use (3) transform (2) algorithm (2) supervis (2)

Person: James T. Kwok

DBLP: Kwok:James_T=

Contributed to:

2014

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2000

Wrote 21 papers:

ICML-c2-2014-ZhangK #distributed #optimisation: Asynchronous Distributed ADMM for Consensus Optimization (RZ, JTK), pp. 1701–1709.
ICML-2011-BiK #classification #multi: MultiLabel Classification on Tree- and DAG-Structured Hierarchies (WB, JTK), pp. 17–24.
ICML-2011-ZhongK #automation #modelling #performance: Efficient Sparse Modeling with Automatic Feature Grouping (WZ, JTK), pp. 9–16.
ICML-2010-LiKL #approximate #scalability: Making Large-Scale Nyström Approximation Possible (ML, JTK, BLL), pp. 631–638.
ICML-2009-LiKZ #learning #using: Semi-supervised learning using label mean (YFL, JTK, ZHZ), pp. 633–640.
ICML-2009-ZhangKP #learning #prototype #scalability: Prototype vector machine for large scale semi-supervised learning (KZ, JTK, BP), pp. 1233–1240.
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.
ICML-2006-CheungK #framework #learning #multi: A regularization framework for multiple-instance learning (PMC, JTK), pp. 193–200.
ICML-2006-DaiYTK #adaptation #classification #nondeterminism: Locally adaptive classification piloted by uncertainty (JD, SY, XT, JTK), pp. 225–232.
ICML-2006-ZhangK #kernel #matrix #performance: Block-quantized kernel matrix for fast spectral embedding (KZ, JTK), pp. 1097–1104.
ICPR-v3-2006-LiPKZ #multimodal #using: Multimodal Registration using the Discrete Wavelet Frame Transform (SL, JP, JTK, JZ), pp. 877–880.
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-2004-ZhangKY #algorithm: Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model (ZZ, JTK, DYY).
ICML-2004-ZhangYK #algorithm #kernel #learning #matrix #using: Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm (ZZ, DYY, JTK).
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.
ICPR-v2-2002-ZhuKQ: Improving De-Noising by Coefficient De-Noising and Dyadic Wavelet Transform (HZ, JTK, LQ), p. 273–?.
ICPR-v2-2000-LawK #clustering #learning #modelling #sequence: Rival Penalized Competitive Learning for Model-Based Sequence Clustering (MHCL, JTK), pp. 2195–2198.