Travelled to:
1 × Australia
1 × Ireland
1 × Korea
1 × Poland
1 × Portugal
1 × Sweden
1 × United Kingdom
2 × France
2 × Italy
4 × China
5 × USA
Collaborated with:
Y.Liu C.Wang S.Song L.Wen J.Sun Z.Lin G.Ding Z.Wang P.S.Yu J.Zhang X.Zhu M.Long A.Zhang Z.Yin M.Hu X.Lin W.Xiang F.Guo D.Li Y.Cao M.Jordan L.Chen J.X.Yu G.Luo D.Xu X.Han Y.Chen X.Lian L.Zou S.Tan R.K.Wong Q.Guo W.Zheng Z.Liu Y.Bai Y.Ning Y.Liu W.Wang H.Wang H.Li W.Tian J.Xu R.Li
Talks about:
predict (4) model (4) learn (4) data (4) use (4) workflow (3) process (3) network (3) classif (3) label (3)
Person: Jianmin Wang
DBLP: Wang:Jianmin
Contributed to:
Wrote 23 papers:
- ICML-2015-LongC0J #adaptation #learning #network
- Learning Transferable Features with Deep Adaptation Networks (ML, YC, JW, MJ), pp. 97–105.
- SIGMOD-2015-SongZWY #constraints #named
- SCREEN: Stream Data Cleaning under Speed Constraints (SS, AZ, JW, PSY), pp. 827–841.
- VLDB-2015-SongZC0 #similarity
- Enriching Data Imputation with Extensive Similarity Neighbors (SS, AZ, LC, JW), pp. 1286–1297.
- VLDB-2015-ZhangWWY14 #behaviour #predict #social
- Inferring Continuous Dynamic Social Influence and Personal Preference for Temporal Behavior Prediction (JZ, CW, JW, JXY), pp. 269–280.
- ICML-c2-2014-LinDH0 #classification #encoding #multi
- Multi-label Classification via Feature-aware Implicit Label Space Encoding (ZL, GD, MH, JW), pp. 325–333.
- ICPR-2014-Xiang0L #classification #hybrid #image
- Local Hybrid Coding for Image Classification (WX, JW, ML), pp. 3744–3749.
- SIGMOD-2014-ZhuSL0Z
- Matching heterogeneous event data (XZ, SS, XL, JW, LZ), pp. 1211–1222.
- KDD-2013-ZhangWNLWY #how #named #network #predict #social #visualisation
- LAFT-Explorer: inferring, visualizing and predicting how your social network expands (JZ, CW, YN, YL, JW, PSY), pp. 1510–1513.
- SIGIR-2013-ZhangWYW #learning #network #predict
- Learning latent friendship propagation networks with interest awareness for link prediction (JZ, CW, PSY, JW), pp. 63–72.
- VLDB-2013-0001SZL #performance
- Efficient Recovery of Missing Events (JW, SS, XZ, XL), pp. 841–852.
- CIKM-2012-LinDH0S #automation #image #random #using #visual notation
- Automatic image annotation using tag-related random search over visual neighbors (ZL, GD, MH, JW, JS), pp. 1784–1788.
- ICEIS-v3-2012-WangWZLW #detection #modelling #process
- Detecting Infeasible Traces in Process Models (ZW, LW, XZ, YL, JW), pp. 212–217.
- SAC-2012-WangTWWG #algorithm #behaviour #empirical #evaluation #mining #process
- An empirical evaluation of process mining algorithms based on structural and behavioral similarities (JW, ST, LW, RKW, QG), pp. 211–213.
- CIKM-2011-WangZLBW #classification #multi #random #using
- Using random walks for multi-label classification (CW, WZ, ZL, YB, JW), pp. 2197–2200.
- ICEIS-v3-2011-WangWWL #modelling #using #workflow
- Formally Modeling and Analyzing Data-centric Workflow using WFCP-net and ASK-CTL (ZW, JW, LW, GL), pp. 139–144.
- SIGIR-2011-LinDW #image #recommendation
- Image annotation based on recommendation model (ZL, GD, JW), pp. 1097–1098.
- SIGMOD-2010-WangWLWWLTXL #dataset #detection #named
- MapDupReducer: detecting near duplicates over massive datasets (CW, JW, XL, WW, HW, HL, WT, JX, RL), pp. 1119–1122.
- ICEIS-ISAS-2009-WangWWL #canonical #modelling #process
- Deriving Canonical Business Object Operation Nets from Process Models (ZW, JW, LW, YL), pp. 182–187.
- ICEIS-AIDSS-2007-YingboJJ #learning #predict #process #using #workflow
- Using Decision Tree Learning to Predict Workflow Activity Time Consumption (YL, JW, JS), pp. 69–75.
- SAC-2007-YingboJJ #approach #machine learning #workflow
- A machine learning approach to semi-automating workflow staff assignment (YL, JW, JS), pp. 340–345.
- SAC-2006-GuoWL #database #relational
- Fingerprinting relational databases (FG, JW, DL), pp. 487–492.
- SEKE-2006-YinW #programming
- Organizational Programming: Hierarchy Software Construction (ZY, JW), pp. 182–187.
- TOOLS-ASIA-2000-XuHWC #corba #persistent #web
- A Strategy for Persistent Object Service under CORBA and WEB Environment (DX, XH, JW, YC), pp. 100–107.