Travelled to:1 × Australia
1 × Canada
1 × Chile
1 × France
1 × Italy
1 × Portugal
1 × South Korea
1 × Spain
5 × USA
Collaborated with:S.Kramer A.Bifet G.Holmes M.Seeland ∅ J.Read R.Gavaldà F.Bravo-Marquez E.Frank J.Wicker H.Bensusan C.G.Giraud-Carrier C.Helma D.Ienco P.Poncelet F.Buchwald R.Clayton J.G.Cleary M.Utting Samuel Sarjant K.Driessens Tony Smith G.D.F.Morales G.Holmes R.Kirkby H.Kremer P.Kranen T.Jansen T.Seidl
Talks about:stream (6) data (6) learn (5) evolv (4) base (4) effici (3) evalu (3) determin (2) compress (2) various (2)
Person: Bernhard Pfahringer
DBLP: Pfahringer:Bernhard
Contributed to:
2015
2015
20142013201220112009
2002
20001997199619951995
2011Wrote 17 papers:
- KDD-2015-BifetMRHP #big data #classification #data type #evaluation #online #performance
- Efficient Online Evaluation of Big Data Stream Classifiers (AB, GDFM, JR, GH, BP), pp. 59–68.
- SIGIR-2015-Bravo-MarquezFP #twitter #word
- From Unlabelled Tweets to Twitter-specific Opinion Words (FBM, EF, BP), pp. 743–746.
- SAC-2014-IencoBPP #category theory #data type #detection #evolution
- Change detection in categorical evolving data streams (DI, AB, BP, PP), pp. 792–797.
- SAC-2013-BifetPRH #adaptation #classification #data type #performance #probability
- Efficient data stream classification via probabilistic adaptive windows (AB, BP, JR, GH), pp. 801–806.
- SAC-2013-SeelandKP #graph #kernel #learning
- Model selection based product kernel learning for regression on graphs (MS, SK, BP), pp. 136–143.
- SAC-2012-SeelandBKP
- Maximum Common Subgraph based locally weighted regression (MS, FB, SK, BP), pp. 165–172.
- SAC-2012-WickerPK #classification #composition #matrix #multi #using
- Multi-label classification using boolean matrix decomposition (JW, BP, SK), pp. 179–186.
- KDD-2011-BifetHPG #data type #evolution #graph #mining
- Mining frequent closed graphs on evolving data streams (AB, GH, BP, RG), pp. 591–599.
- KDD-2011-KremerKJSBHP #clustering #data type #effectiveness #evaluation #evolution
- An effective evaluation measure for clustering on evolving data streams (HK, PK, TJ, TS, AB, GH, BP), pp. 868–876.
- KDD-2009-BifetHPKG #data type #evolution
- New ensemble methods for evolving data streams (AB, GH, BP, RK, RG), pp. 139–148.
- LOPSTR-2002-ClaytonCPU #bottom-up #logic programming
- Tabling Structures for Bottom-Up Logic Programming (RC, JGC, BP, MU), pp. 50–51.
- ICML-2000-PfahringerBG #algorithm #learning
- Meta-Learning by Landmarking Various Learning Algorithms (BP, HB, CGGC), pp. 743–750.
- KDD-1997-KramerPH #machine learning #mining
- Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail (SK, BP, CH), pp. 223–226.
- KDD-1996-KramerP #performance
- Efficient Search for Strong Partial Determinations (SK, BP), pp. 371–374.
- ICML-1995-Pfahringer
- Compression-Based Discretization of Continuous Attributes (BP), pp. 456–463.
- KDD-1995-PfahringerK #evaluation
- Compression-Based Evaluation of Partial Determinations (BP, SK), pp. 234–239.
- CIG-2011-SarjantPDS #game studies #online #policy #relational #using
- Using the online cross-entropy method to learn relational policies for playing different games (SS, BP, KD, TS), pp. 182–189.