Travelled to:1 × China
1 × France
2 × Canada
3 × USA
Collaborated with:J.T.Kwok D.Yeung S.Wang H.Qian ∅ A.Jain E.Y.Chang L.Luo Y.Xie W.Li B.Tu C.Zhang Y.Zhang D.Wang G.Wang W.Chen B.Hu L.Zhang
Talks about:learn (3) discrimin (2) algorithm (2) bayesian (2) matrix (2) kernel (2) model (2) infer (2) data (2) use (2)
Person: Zhihua Zhang
DBLP: Zhang:Zhihua
Contributed to:
20152014
2014
2010200620042003Wrote 8 papers:
- ICML-2015-LuoXZL #matrix
- Support Matrix Machines (LL, YX, ZZ, WJL), pp. 938–947.
- ICML-c2-2014-TuZWQ #analysis #scalability
- Making Fisher Discriminant Analysis Scalable (BT, ZZ, SW, HQ), pp. 964–972.
- KDD-2014-WangZQZ #using
- Improving the modified nyström method using spectral shifting (SW, CZ, HQ, ZZ), pp. 611–620.
- CIKM-2010-ZhangWWCZHZ #learning #modelling
- Learning click models via probit bayesian inference (YZ, DW, GW, WC, ZZ, BH, LZ), pp. 439–448.
- CIKM-2006-JainZC #adaptation #clustering #data type
- Adaptive non-linear clustering in data streams (AJ, ZZ, EYC), pp. 122–131.
- 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-Zhang #kernel #learning #metric #multi #representation #scalability #towards
- Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean Representation (ZZ), pp. 872–879.