Travelled to:
1 × Canada
1 × Japan
1 × Sweden
4 × USA
Collaborated with:
M.Menarini W.G.Griswold H.Wang N.Sebe R.Rosales G.Fung J.G.Dy Y.Zhang O.Lanz E.Ricci R.Subramanian G.Liu C.Lyu S.Ramanathan G.Liu X.Yin Z.Wang X.Yin C.Yang H.Hao
Talks about:
multi (4) code (4) classif (3) learn (3) view (3) recognit (2) softwar (2) robust (2) activ (2) task (2)
Person: Yan Yan
DBLP: Yan:Yan
Contributed to:
Wrote 11 papers:
- ICPR-2014-YanRLSS #analysis #clustering #invariant #linear #multi #recognition
- Clustered Multi-task Linear Discriminant Analysis for View Invariant Color-Depth Action Recognition (YY, ER, GL, RS, NS), pp. 3493–3498.
- ICPR-2014-YanSRLS #classification #interactive #learning #multi
- Evaluating Multi-task Learning for Multi-view Head-Pose Classification in Interactive Environments (YY, RS, ER, OL, NS), pp. 4182–4187.
- ICSME-2014-YanMG #contract #evolution #mining
- Mining Software Contracts for Software Evolution (YY, MM, WGG), pp. 471–475.
- ICDAR-2013-YanYWYYH #classification #sorting
- Sorting-Based Dynamic Classifier Ensemble Selection (YY, XCY, ZBW, XY, CY, HWH), pp. 673–677.
- ICPR-2012-LiuW12b #performance #recognition #robust
- Accelerated robust sparse coding for fast face recognition (GL, YY, HW), pp. 3394–3397.
- ICPR-2012-LyuYW #metric #robust #visual notation
- Robust visual tracking with the cross-bin metric (CL, YY, HW), pp. 2120–2123.
- ICPR-2012-YanRLS #classification #learning #multi
- Active transfer learning for multi-view head-pose classification (YY, SR, OL, NS), pp. 1168–1171.
- ICML-2011-YanRFD #learning
- Active Learning from Crowds (YY, RR, GF, JGD), pp. 1161–1168.
- KDD-2010-YanFDR #classification
- Medical coding classification by leveraging inter-code relationships (YY, GF, JGD, RR), pp. 193–202.
- ICPR-2008-YanZ #correlation #multimodal #using
- Multimodal biometrics fusion using Correlation Filter Bank (YY, YJZ), pp. 1–4.
- ASE-2017-MenariniYG #case study #code review #overview #performance #semantics #user study
- Semantics-assisted code review: an efficient toolchain and a user study (MM, YY, WGG), pp. 554–565.