BibSLEIGH — Pang-Ning

Travelled to:

1 × China
1 × Japan
2 × Canada
2 × France
8 × USA

Collaborated with:

V.Kumar F.Chen H.Cheng M.Steinbach P.M.Comar J.Scripps A.Esfahanian H.Xiong R.Jin L.Liu S.Saha A.Nucci S.Ranjan A.K.Jain A.K.Jain S.J.Fodeh W.F.Punch J.Gao J.Srivastava S.Shekhar H.Blau S.A.Harp R.P.Goldman S.A.Klooster C.Potter

Talks about:

detect (4) learn (4) support (3) pattern (3) network (3) supervis (2) multipl (2) cluster (2) measur (2) featur (2)

Person: Pang-Ning Tan

DBLP: Tan:Pang=Ning

Contributed to:

2012

2011

2010

2009

2008

2006

2004

2003

2002

2000

Wrote 17 papers:

CIKM-2012-ComarLSNT #detection #kernel #linear: Weighted linear kernel with tree transformed features for malware detection (PMC, LL, SS, AN, PNT), pp. 2287–2290.
ICPR-2012-LiuCSTN #learning #multi #performance #problem #recursion #scalability: Recursive NMF: Efficient label tree learning for large multi-class problems (LL, PMC, SS, PNT, AN), pp. 2148–2151.
KDD-2011-ChenRT #adaptation #detection #incremental #learning: Detecting bots via incremental LS-SVM learning with dynamic feature adaptation (FC, SR, PNT), pp. 386–394.
CIKM-2010-ComarTJ #learning #multi #network: Multi task learning on multiple related networks (PMC, PNT, AKJ), pp. 1737–1740.
CIKM-2009-ChenTJ #classification #detection #framework #social #social media #web: A co-classification framework for detecting web spam and spammers in social media web sites (FC, PNT, AKJ), pp. 1807–1810.
KDD-2009-ScrippsTE #analysis #network #preprocessor: Measuring the effects of preprocessing decisions and network forces in dynamic network analysis (JS, PNT, AHE), pp. 747–756.
SAC-2009-FodehPT #clustering #documentation #semantics #statistics: Combining statistics and semantics via ensemble model for document clustering (SJF, WFP, PNT), pp. 1446–1450.
ICPR-2008-ScrippsTCE #approach #matrix #predict: A matrix alignment approach for link prediction (JS, PNT, FC, AHE), pp. 1–4.
KDD-2008-ChengT #learning: Semi-supervised learning with data calibration for long-term time series forecasting (HC, PNT), pp. 133–141.
SAC-2006-GaoCT #detection: Semi-supervised outlier detection (JG, HC, PNT), pp. 635–636.
KDD-2004-SteinbachTK: Support envelopes: a technique for exploring the structure of association patterns (MS, PNT, VK), pp. 296–305.
KDD-2004-SteinbachTXK: Generalizing the notion of support (MS, PNT, HX, VK), pp. 689–694.
KDD-2004-TanJ #multi: Ordering patterns by combining opinions from multiple sources (PNT, RJ), pp. 695–700.
KDD-2004-XiongSTK #bound #correlation #identification: Exploiting a support-based upper bound of Pearson’s correlation coefficient for efficiently identifying strongly correlated pairs (HX, SS, PNT, VK), pp. 334–343.
KDD-2003-SteinbachTKKP #clustering #using: Discovery of climate indices using clustering (MS, PNT, VK, SAK, CP), pp. 446–455.
KDD-2002-TanKS: Selecting the right interestingness measure for association patterns (PNT, VK, JS), pp. 32–41.
KDD-2000-TanBHG #data mining #mining: Textual data mining of service center call records (PNT, HB, SAH, RPG), pp. 417–423.