Person: Stefan Kramer

DBLP: Kramer:Stefan

Contributed to:

2015

2015

2014

2014

2014

2013

2012

2012

2009

2006

2004

2004

2003

2002

2001

2001

1999

1998

1998

1997

1997

1996

1995

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.

SAC-2015-FrankM0

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.

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-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.

ICML-2002-BringmannKNPW

Transformation-Based Regression (BB, SK, FN, HP, GW), pp. 59–66.

ICML-2001-KramerR

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.