BibSLEIGH — Xingquan

Travelled to:

1 × Brazil
1 × Canada
1 × Japan
1 × United Kingdom
2 × China
8 × USA

Collaborated with:

S.Pan X.Wu M.Fang P.Zhang C.Zhang Z.Zhu Y.Guo X.Xue T.Guo L.Guo Y.Ye J.Yin C.Bao W.Qiu Y.Shi Y.Yang Q.Chen G.Long L.Chen Y.Fu B.Li J.Tan Z.Lu J.Bongard Y.Lee A.S.Pandya S.Hsu X.Su T.M.Khoshgoftaar R.Greiner J.Wu Z.Hong Z.Cai J.Li P.Wang B.J.Gao

Talks about:

data (9) stream (8) learn (7) graph (5) classif (4) queri (4) activ (4) transfer (3) featur (3) label (3)

Person: Xingquan Zhu

DBLP: Zhu:Xingquan

Contributed to:

2014

2013

2012

2011

2010

2008

2006

2005

2003

Wrote 22 papers:

CIKM-2014-WuHPZCZ #feature model #learning #multi: Exploring Features for Complicated Objects: Cross-View Feature Selection for Multi-Instance Learning (JW, ZH, SP, XZ, ZC, CZ), pp. 1699–1708.
CIKM-2013-FangYZ #graph #scalability: Active exploration: simultaneous sampling and labeling for large graphs (MF, JY, XZ), pp. 829–834.
CIKM-2013-GuoZ #classification #comprehension #empirical #graph #perspective: Understanding the roles of sub-graph features for graph classification: an empirical study perspective (TG, XZ), pp. 817–822.
CIKM-2012-LongCZZ #classification #named #using: TCSST: transfer classification of short & sparse text using external data (GL, LC, XZ, CZ), pp. 764–772.
CIKM-2012-PanZ #correlation #data type #graph #named #query: CGStream: continuous correlated graph query for data streams (SP, XZ), pp. 1183–1192.
CIKM-2012-PanZ12a #graph #query: Continuous top-k query for graph streams (SP, XZ), pp. 2659–2662.
CIKM-2012-ZhuZYGX #classification #parallel: Parallel proximal support vector machine for high-dimensional pattern classification (ZZ, XZ, YY, YFG, XX), pp. 2351–2354.
ICPR-2012-FangZ #learning: I don’t know the label: Active learning with blind knowledge (MF, XZ), pp. 2238–2241.
ICPR-2012-PanZF #correlation #data type #query: Top-k correlated subgraph query for data streams (SP, XZ, MF), pp. 2906–2909.
CIKM-2011-FuLZZ #learning #query: Do they belong to the same class: active learning by querying pairwise label homogeneity (YF, BL, XZ, CZ), pp. 2161–2164.
CIKM-2011-ZhuZYGX #learning: Transfer active learning (ZZ, XZ, YY, YFG, XX), pp. 2169–2172.
KDD-2011-ZhangLWGZG #data type #modelling #performance #predict: Enabling fast prediction for ensemble models on data streams (PZ, JL, PW, BJG, XZ, LG), pp. 177–185.
CIKM-2010-ZhangZTG #concept #data type #framework #named: SKIF: a data imputation framework for concept drifting data streams (PZ, XZ, JT, LG), pp. 1869–1872.
CIKM-2010-ZhuZGX #classification #incremental #learning: Transfer incremental learning for pattern classification (ZZ, XZ, YFG, XX), pp. 1709–1712.
KDD-2010-LuWZB: Ensemble pruning via individual contribution ordering (ZL, XW, XZ, JB), pp. 871–880.
ICPR-2008-ZhuBQ #lazy evaluation #learning: Bagging very weak learners with lazy local learning (XZ, CB, WQ), pp. 1–4.
KDD-2008-ZhangZS #categorisation #concept #data type #mining: Categorizing and mining concept drifting data streams (PZ, XZ, YS), pp. 812–820.
SAC-2008-LeeZPH #evaluation #interactive #named #testing #visualisation: iVESTA: an interactive visualization and evaluation system for drive test data (YL, XZ, ASP, SH), pp. 1953–1957.
SAC-2008-SuKZG #classification #collaboration #machine learning #using: Imputation-boosted collaborative filtering using machine learning classifiers (XS, TMK, XZ, RG), pp. 949–950.
ICPR-v3-2006-ZhuW #ranking #scalability: Scalable Representative Instance Selection and Ranking (XZ, XW), pp. 352–355.
KDD-2005-YangWZ #data type #predict: Combining proactive and reactive predictions for data streams (YY, XW, XZ), pp. 710–715.
ICML-2003-ZhuWC #dataset #scalability: Eliminating Class Noise in Large Datasets (XZ, XW, QC), pp. 920–927.