Travelled to:1 × Brazil
1 × China
1 × Japan
1 × Switzerland
1 × United Kingdom
2 × USA
Collaborated with:J.Yan Z.Chen S.Yan Y.Lin W.Zhang L.Ji W.Fan B.Zhang W.Ma D.Shen D.Chen Y.Li Y.Gu H.Liu J.He J.Tang Y.Shen S.Guo B.Gao M.Zhang Q.Cheng Q.Yang F.Bai
Talks about:search (4) learn (3) orthogon (2) behavior (2) audienc (2) target (2) rank (2) ad (2) pedestrian (1) substitut (1)
Person: Ning Liu
DBLP: Liu:Ning
Contributed to:
2012
2011
20102009200920052004Wrote 8 papers:
- ICPR-2012-LinL #bottom-up #process #top-down
- Integrating bottom-up and top-down processes for accurate pedestrian counting (YL, NL), pp. 2508–2511.
- CIKM-2011-GuYLHJLC
- Extract knowledge from semi-structured websites for search task simplification (YG, JY, HL, JH, LJ, NL, ZC), pp. 1883–1888.
- CIKM-2011-TangLYSGGYZ #behaviour #learning #rank
- Learning to rank audience for behavioral targeting in display ads (JT, NL, JY, YS, SG, BG, SY, MZ), pp. 605–610.
- SIGIR-2010-LiuYSCCL #behaviour #learning #rank
- Learning to rank audience for behavioral targeting (NL, JY, DS, DC, ZC, YL), pp. 719–720.
- CIKM-2009-JiYLZFC #named #novel #online
- ExSearch: a novel vertical search engine for online barter business (LJ, JY, NL, WZ, WF, ZC), pp. 1357–1366.
- SIGIR-2009-ZhangYYLC #query
- Temporal query substitution for ad search (WZ, JY, SY, NL, ZC), pp. 798–799.
- SIGIR-2005-YanLZYCCFM #categorisation #feature model #named #orthogonal
- OCFS: optimal orthogonal centroid feature selection for text categorization (JY, NL, BZ, SY, ZC, QC, WF, WYM), pp. 122–129.
- CIKM-2004-LiuZYYYCBM #learning #metric #similarity
- Learning similarity measures in non-orthogonal space (NL, BZ, JY, QY, SY, ZC, FB, WYM), pp. 334–341.