BibSLEIGH — Wei

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: Fan:Wei

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2004

2003

1999

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.