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Travelled to:
1 × Australia
1 × Austria
1 × Canada
1 × France
1 × Japan
1 × Slovenia
1 × Sweden
16 × USA
2 × United Kingdom
3 × China
Collaborated with:
P.S.Yu J.Han J.Gao J.Ye J.Sun S.Naoi I.Davidson H.Tong Q.Li B.Zhao E.Zhong H.Wang S.J.Stolfo J.Zhang R.Chattopadhyay S.Panchanathan J.Ni X.Zhang S.Zhang Y.Yang Y.Li Y.Sun Y.He Y.Hotta D.S.Turaga Z.Wang J.Wang S.Yang P.Wonka X.Wang H.Liu X.Kong H.Kargupta J.Gama J.McCloskey X.Yan L.Su K.Zhang O.Verscheure P.Gong J.Zhou J.Zhang G.Tian Y.Mu M.Winslett Y.Zhu Q.Yang X.Shi J.Zhang J.Jiang Y.Wang W.Liu T.Tan S.Park P.K.Chan Y.Cai P.Ji Q.He S.Xie L.Lan M.Yuan W.Dong L.Shi C.Zhou C.Wu J.Wang L.Xiao Y.Li A.Minagawa M.Lai Z.Lu H.Davulcu Q.Li L.Wang Y.Katsuyama S.Xiang L.Yuan Y.Wang P.M.Thompson X.Zhang F.Liang C.Wang B.Wang J.Tang S.Chen Z.Yang Y.Liu J.Peng J.Ren M.Demirbas H.Cheng K.Wu B.Gedik K.Hildrum C.C.Aggarwal E.Bouillet D.George X.Gu G.Luo
Talks about:
data (12) network (9) mine (7) learn (6) heterogen (5) stream (5) select (5) sourc (5) multi (5) model (5)

Person: Wei Fan

DBLP DBLP: Fan:Wei

Contributed to:

DRR 20152015
KDD 20152015
VLDB 20152015
VLDB 20152014
DocEng 20142014
ICML c2 20142014
ICPR 20142014
KDD 20142014
SIGMOD 20142014
VLDB 20142014
ICML c3 20132013
KDD 20132013
CIKM 20122012
ICPR 20122012
KDD 20122012
CIKM 20112011
DRR 20112011
KDD 20112011
KDD 20102010
CIKM 20092009
KDD 20092009
KDD 20082008
VLDB 20072007
KDD 20062006
ICPR v1 20042004
KDD 20042004
VLDB 20042004
KDD 20032003
SIGMOD 20032003
ICML 19991999
KDD 19991999

Wrote 47 papers:

DRR-2015-Fan0N #documentation #image #performance
Separation of text and background regions for high performance document image compression (WF, JS, SN).
KDD-2015-CaiTFJH #higher-order #mining #named #performance
Facets: Fast Comprehensive Mining of Coevolving High-order Time Series (YC, HT, WF, PJ, QH), pp. 79–88.
KDD-2015-LiLGSZFH #evolution #on the
On the Discovery of Evolving Truth (YL, QL, JG, LS, BZ, WF, JH), pp. 675–684.
KDD-2015-NiTFZ #clustering #flexibility #multi #robust
Flexible and Robust Multi-Network Clustering (JN, HT, WF, XZ), pp. 835–844.
VLDB-2015-GaoLZFH #crowdsourcing #perspective
Truth Discovery and Crowdsourcing Aggregation: A Unified Perspective (JG, QL, BZ, WF, JH), pp. 2048–2059.
VLDB-2015-LiLGSZDFH14 #approach
A Confidence-Aware Approach for Truth Discovery on Long-Tail Data (QL, YL, JG, LS, BZ, MD, WF, JH), pp. 425–436.
DocEng-2014-Fan0N #using
Paper stitching using maximum tolerant seam under local distortions (WF, JS, SN), pp. 35–44.
ICML-c2-2014-WangLLFDY #matrix
Rank-One Matrix Pursuit for Matrix Completion (ZW, MJL, ZL, WF, HD, JY), pp. 91–99.
ICML-c2-2014-WangLYFWY #algorithm #modelling #parallel #scalability
A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models (JW, QL, SY, WF, PW, JY), pp. 235–243.
ICPR-2014-WangFH0KH #detection #image #performance
Fast and Accurate Text Detection in Natural Scene Images with User-Intention (LW, WF, YH, JS, YK, YH), pp. 2920–2925.
KDD-2014-GongZFY #learning #multi #performance
Efficient multi-task feature learning with calibration (PG, JZ, WF, JY), pp. 761–770.
KDD-2014-NiTFZ #network #ranking
Inside the atoms: ranking on a network of networks (JN, HT, WF, XZ), pp. 1356–1365.
KDD-2014-XieGFTY
Class-distribution regularized consensus maximization for alleviating overfitting in model combination (SX, JG, WF, DST, PSY), pp. 303–312.
KDD-2014-ZhangTMF #learning #network
Supervised deep learning with auxiliary networks (JZ, GT, YM, WF), pp. 353–361.
SIGMOD-2014-LiLGZFH #estimation #reliability #semistructured data
Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation (QL, YL, JG, BZ, WF, JH), pp. 1187–1198.
SIGMOD-2014-ZhangYFLY #named #realtime #scalability
OceanRT: real-time analytics over large temporal data (SZ, YY, WF, LL, MY), pp. 1099–1102.
VLDB-2014-ZhangYFW #design #implementation #interactive #realtime #scalability
Design and Implementation of a Real-Time Interactive Analytics System for Large Spatio-Temporal Data (SZ, YY, WF, MW), pp. 1754–1759.
ICML-c3-2013-ChattopadhyayFDPY #learning
Joint Transfer and Batch-mode Active Learning (RC, WF, ID, SP, JY), pp. 253–261.
KDD-2013-XiangYFWTY #learning #multi #predict
Multi-source learning with block-wise missing data for Alzheimer’s disease prediction (SX, LY, WF, YW, PMT, JY), pp. 185–193.
KDD-2013-YangWFZWY #algorithm #multi #performance #problem
An efficient ADMM algorithm for multidimensional anisotropic total variation regularization problems (SY, JW, WF, XZ, PW, JY), pp. 641–649.
KDD-2013-ZhongFZY #modelling #network #social
Modeling the dynamics of composite social networks (EZ, WF, YZ, QY), pp. 937–945.
CIKM-2012-DongFSZY #framework #mining
A general framework to encode heterogeneous information sources for contextual pattern mining (WD, WF, LS, CZ, XY), pp. 65–74.
ICPR-2012-WuFH0N #network #recognition
Cascaded heterogeneous convolutional neural networks for handwritten digit recognition (CW, WF, YH, JS, SN), pp. 657–660.
KDD-2012-ChattopadhyayWFDPY #probability
Batch mode active sampling based on marginal probability distribution matching (RC, ZW, WF, ID, SP, JY), pp. 741–749.
KDD-2012-ZhongFWXL #adaptation #behaviour #named #network #social
ComSoc: adaptive transfer of user behaviors over composite social network (EZ, WF, JW, LX, YL), pp. 696–704.
CIKM-2011-WangLF #network
Connecting users with similar interests via tag network inference (XW, HL, WF), pp. 1019–1024.
DRR-2011-FanSNMH #feature model #recognition
Natural scene logo recognition by joint boosting feature selection in salient regions (WF, JS, SN, AM, YH), pp. 1–10.
KDD-2011-ChattopadhyayYPFD #adaptation #detection #multi
Multi-source domain adaptation and its application to early detection of fatigue (RC, JY, SP, WF, ID), pp. 717–725.
KDD-2011-KongFY #classification #graph
Dual active feature and sample selection for graph classification (XK, WF, PSY), pp. 654–662.
KDD-2011-ShiFZY #evolution #graph
Discovering shakers from evolving entities via cascading graph inference (XS, WF, JZ, PSY), pp. 1001–1009.
KDD-2010-GaoLFWSH #community #detection #network #on the #performance
On community outliers and their efficient detection in information networks (JG, FL, WF, CW, YS, JH), pp. 813–822.
KDD-2010-KarguptaGF #data mining #generative #mining
The next generation of transportation systems, greenhouse emissions, and data mining (HK, JG, WF), pp. 1209–1212.
CIKM-2009-WangTFCYL #ranking
Heterogeneous cross domain ranking in latent space (BW, JT, WF, SC, ZY, YL), pp. 987–996.
KDD-2009-GaoFSH #learning
Heterogeneous source consensus learning via decision propagation and negotiation (JG, WF, YS, JH), pp. 339–348.
KDD-2009-ZhongFPZRTV #adaptation #kernel
Cross domain distribution adaptation via kernel mapping (EZ, WF, JP, KZ, JR, DST, OV), pp. 1027–1036.
KDD-2008-FanZCGYHYV #mining #modelling
Direct mining of discriminative and essential frequent patterns via model-based search tree (WF, KZ, HC, JG, XY, JH, PSY, OV), pp. 230–238.
KDD-2008-GaoFJH #information management #multi
Knowledge transfer via multiple model local structure mapping (JG, WF, JJ, JH), pp. 283–291.
VLDB-2007-WuYGHABFGGLW #challenge #experience #monitoring #multi #prototype
Challenges and Experience in Prototyping a Multi-Modal Stream Analytic and Monitoring Application on System S (KLW, PSY, BG, KH, CCA, EB, WF, DG, XG, GL, HW), pp. 1185–1196.
KDD-2006-FanD #bias #classification #framework #performance #testing
Reverse testing: an efficient framework to select amongst classifiers under sample selection bias (WF, ID), pp. 147–156.
KDD-2006-FanMY #framework #performance #random #summary
A general framework for accurate and fast regression by data summarization in random decision trees (WF, JM, PSY), pp. 136–146.
ICPR-v1-2004-FanWLT #null #recognition
Combining Null Space-based Gabor Features for Face Recognition (WF, YW, WL, TT), pp. 330–333.
KDD-2004-Fan #concept #data type
Systematic data selection to mine concept-drifting data streams (WF), pp. 128–137.
VLDB-2004-Fan #classification #concept #data type #named
StreamMiner: A Classifier Ensemble-based Engine to Mine Concept-drifting Data Streams (WF), pp. 1257–1260.
KDD-2003-WangFYH #classification #concept #data type #mining #using
Mining concept-drifting data streams using ensemble classifiers (HW, WF, PSY, JH), pp. 226–235.
SIGMOD-2003-WangPFY #named #query #xml
ViST: A Dynamic Index Method for Querying XML Data by Tree Structures (HW, SP, WF, PSY), pp. 110–121.
ICML-1999-FanSZC #classification #named
AdaCost: Misclassification Cost-Sensitive Boosting (WF, SJS, JZ, PKC), pp. 97–105.
KDD-1999-FanSZ #distributed #learning #online #scalability
The Application of AdaBoost for Distributed, Scalable and On-Line Learning (WF, SJS, JZ), pp. 362–366.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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