BibSLEIGH corpus
BibSLEIGH tags
BibSLEIGH bundles
BibSLEIGH people
Open Knowledge
XHTML 1.0 W3C Rec
CSS 2.1 W3C CanRec
email twitter
Travelled to:
1 × China
1 × Cyprus
1 × France
1 × Germany
1 × Italy
1 × South Korea
1 × Spain
10 × USA
2 × Australia
2 × Canada
2 × Portugal
Collaborated with:
B.Pfahringer U.Rückert M.Seeland C.Helma H.Kaindl A.Karwath E.Frank L.D.Raedt A.Maunz S.Burkhardt J.Wicker M.Mayo J.Shao Z.Ahmadi A.Hapfelmeier J.Schmidt P.S.N.Diallo L.M.Afonso R.Kacsich C.Welsch A.Schalk E.Hoos C.Gröger B.Mitschang F.Buchwald B.Bringmann F.Neubarth H.Pirker G.Widmer N.Krauter B.Derstorff C.Stönner E.Bourtsoukidis T.Klüpfel J.Williams
Talks about:
learn (7) base (6) graph (5) search (4) mine (4) data (4) structur (3) regress (3) classif (3) multi (3)

Person: Stefan Kramer

DBLP DBLP: Kramer:Stefan

Contributed to:

KDD 20152015
SAC 20152015
ICEIS v2 20142014
KDD 20142014
SAC 20142014
SAC 20132013
KDD 20122012
SAC 20122012
KDD 20092009
ICML 20062006
ICML 20042004
SAC 20042004
ICML 20032003
ICML 20022002
ICML 20012001
KDD 20012001
HT 19991999
HT 19981998
ICRE 19981998
ICSE 19971997
KDD 19971997
KDD 19961996
KDD 19951995

Wrote 28 papers:

KDD-2015-WickerKDSBKW0 #data mining #mining #smell
Cinema Data Mining: The Smell of Fear (JW, NK, BD, CS, EB, TK, JW, SK), pp. 1295–1304.
SAC-2015-BurkhardtK #classification #multi #on the
On the spectrum between binary relevance and classifier chains in multi-label classification (SB, SK), pp. 885–892.
Alternating model trees (EF, MM, SK), pp. 871–878.
ICEIS-v2-2014-HoosGKM #analysis #framework #identification #mobile #process
Improving Business Processes Through Mobile Apps — An Analysis Framework to Identify Value-added App Usage Scenarios (EH, CG, SK, BM), pp. 71–82.
KDD-2014-ShaoAK #concept #data type #learning #prototype
Prototype-based learning on concept-drifting data streams (JS, ZA, SK), pp. 412–421.
SAC-2014-SeelandKK #clustering #graph
Structural clustering of millions of molecular graphs (MS, AK, SK), pp. 121–128.
SAC-2014-SeelandMKK #classification
Extracting information from support vector machines for pattern-based classification (MS, AM, AK, SK), pp. 129–136.
SAC-2013-HapfelmeierSK #dataset #incremental #linear #performance
Incremental linear model trees on massive datasets: keep it simple, keep it fast (AH, JS, SK), pp. 129–135.
SAC-2013-SeelandKP #graph #kernel #learning
Model selection based product kernel learning for regression on graphs (MS, SK, BP), pp. 136–143.
KDD-2012-SeelandKK #clustering #graph #kernel #learning
A structural cluster kernel for learning on graphs (MS, AK, SK), pp. 516–524.
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-2009-MaunzHK #graph #mining #refinement #scalability #using
Large-scale graph mining using backbone refinement classes (AM, CH, SK), pp. 617–626.
ICML-2006-RuckertK #approach #learning #statistics
A statistical approach to rule learning (UR, SK), pp. 785–792.
ICML-2004-FrankK #multi #problem
Ensembles of nested dichotomies for multi-class problems (EF, SK).
ICML-2004-RuckertK #bound #learning #towards
Towards tight bounds for rule learning (UR, SK).
SAC-2004-RuckertK #graph
Frequent free tree discovery in graph data (UR, SK), pp. 564–570.
ICML-2003-RuckertK #learning #probability
Stochastic Local Search in k-Term DNF Learning (UR, SK), pp. 648–655.
Transformation-Based Regression (BB, SK, FN, HP, GW), pp. 59–66.
Feature Construction with Version Spaces for Biochemical Applications (SK, LDR), pp. 258–265.
KDD-2001-KramerRH #mining
Molecular feature mining in HIV data (SK, LDR, CH), pp. 136–143.
HT-1999-KaindlKD #generative #taxonomy
Semiautomatic Generation of Glossary Links: A Practical Solution (HK, SK, PSND), pp. 3–12.
HT-1998-KaindlKA #web
Combining Structure Search and Content Search for the World-Wide Web (HK, SK, LMA), pp. 217–224.
ICRE-1998-KaindlKK #case study #functional #requirements #using
A Case Study of Decomposing Functional Requirements Using Scenarios (HK, SK, RK), pp. 156–163.
ICSE-1997-WelschSK #object-oriented #re-engineering
Integrating Forward and Reverse Object-Oriented Software Engineering (CW, AS, SK), pp. 560–561.
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.
KDD-1995-PfahringerK #evaluation
Compression-Based Evaluation of Partial Determinations (BP, SK), pp. 234–239.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.