2010 | ||
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71 | Mario Boley, Tamás Horváth, Axel Poigné, Stefan Wrobel: Listing closed sets of strongly accessible set systems with applications to data mining. Theor. Comput. Sci. 411(3): 691-700 (2010) | |
2009 | ||
70 | Dennis Wegener, Michael Mock, Deyaa Adranale, Stefan Wrobel: Toolkit-Based High-Performance Data Mining of Large Data on MapReduce Clusters. ICDM Workshops 2009: 296-301 | |
69 | Hongqi Wang, Olana Missura, Thomas Gärtner, Stefan Wrobel: Context-Based Clustering of Image Search Results. KI 2009: 153-160 | |
68 | Mario Boley, Tamás Horváth, Stefan Wrobel: Efficient Discovery of Interesting Patterns Based on Strong Closedness. SDM 2009: 1002-1013 | |
67 | Mario Boley, Tamás Horváth, Stefan Wrobel: Efficient discovery of interesting patterns based on strong closedness. Statistical Analysis and Data Mining 2(5-6): 346-360 (2009) | |
2008 | ||
66 | Henrik Grosskreutz, Stefan Rüping, Stefan Wrobel: Tight Optimistic Estimates for Fast Subgroup Discovery. ECML/PKDD (1) 2008: 440-456 | |
65 | Natalja Punko, Stefan Rüping, Stefan Wrobel: Facilitating Clinico-Genomic Knowledge Discovery by Automatic Selection of KDD Processes. LWA 2008: 84-86 | |
64 | Gennady L. Andrienko, Natalia V. Andrienko, Ioannis Kopanakis, Arend Ligtenberg, Stefan Wrobel: Visual Analytics Methods for Movement Data. Mobility, Data Mining and Privacy 2008: 375-410 | |
2007 | ||
63 | Mario Boley, Tamás Horváth, Axel Poigné, Stefan Wrobel: Efficient Closed Pattern Mining in Strongly Accessible Set Systems. MLG 2007 | |
62 | Shankar Vembu, Thomas Gärtner, Stefan Wrobel: Semidefinite Ranking on Graphs. MLG 2007 | |
61 | Mario Boley, Tamás Horváth, Axel Poigné, Stefan Wrobel: Efficient Closed Pattern Mining in Strongly Accessible Set Systems (Extended Abstract). PKDD 2007: 382-389 | |
60 | Gennady L. Andrienko, Natalia V. Andrienko, Piotr Jankowski, Daniel A. Keim, Menno-Jan Kraak, Alan M. MacEachren, Stefan Wrobel: Geovisual analytics for spatial decision support: Setting the research agenda. International Journal of Geographical Information Science 21(8): 839-857 (2007) | |
59 | Gennady L. Andrienko, Natalia V. Andrienko, Stefan Wrobel: Visual analytics tools for analysis of movement data. SIGKDD Explorations 9(2): 38-46 (2007) | |
2006 | ||
58 | Christine Körner, Stefan Wrobel: Multi-class Ensemble-Based Active Learning. ECML 2006: 687-694 | |
57 | Ulf Brefeld, Thomas Gärtner, Tobias Scheffer, Stefan Wrobel: Efficient co-regularised least squares regression. ICML 2006: 137-144 | |
56 | Tamás Horváth, Jan Ramon, Stefan Wrobel: Frequent subgraph mining in outerplanar graphs. KDD 2006: 197-206 | |
55 | Tamás Horváth, Jan Ramon, Stefan Wrobel: Frequent Subgraph Mining in Outerplanar Graphs. LWA 2006: 290-296 | |
54 | Christine Körner, Stefan Wrobel: Bias-Free Hypothesis Evaluation in Multirelational Domains. PAKDD 2006: 668-672 | |
53 | Tamás Horváth, Susanne Hoche, Stefan Wrobel: Effective rule induction from labeled graphs. SAC 2006: 611-616 | |
52 | Christine Körner, Stefan Wrobel: Bias-free hypothesis evaluation in multirelational domains. SAC 2006: 639-640 | |
2005 | ||
51 | Luc De Raedt, Stefan Wrobel: Machine Learning, Proceedings of the Twenty-Second International Conference (ICML 2005), Bonn, Germany, August 7-11, 2005 ACM 2005 | |
50 | Stefan Wrobel, Thomas Gärtner, Tamás Horváth: Kernels for Predictive Graph Mining. GfKl 2005: 75-86 | |
49 | Christine Körner, Stefan Wrobel: Bias-free Hypothesis Evaluation in Multirelational Domains. LWA 2005: 172-177 | |
2004 | ||
48 | Lourdes Peña Castillo, Stefan Wrobel: A comparative study on methods for reducing myopia of hill-climbing search in multirelational learning. ICML 2004 | |
47 | Tamás Horváth, Thomas Gärtner, Stefan Wrobel: Cyclic pattern kernels for predictive graph mining. KDD 2004: 158-167 | |
2003 | ||
46 | Thomas Gärtner, Peter A. Flach, Stefan Wrobel: On Graph Kernels: Hardness Results and Efficient Alternatives. COLT 2003: 129-143 | |
45 | Lourdes Peña Castillo, Stefan Wrobel: Learning Minesweeper with Multirelational Learning. IJCAI 2003: 533-540 | |
44 | Susanne Hoche, Stefan Wrobel: A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boosting. ILP 2003: 180-196 | |
43 | Mark-A. Krogel, Simon Rawles, Filip Zelezný, Peter A. Flach, Nada Lavrac, Stefan Wrobel: Comparative Evaluation of Approaches to Propositionalization. ILP 2003: 197-214 | |
42 | Saso Dzeroski, Luc De Raedt, Stefan Wrobel: Multirelational data mining 2003: workshop report. SIGKDD Explorations 5(2): 200-202 (2003) | |
2002 | ||
41 | Mark-A. Krogel, Stefan Wrobel: Feature Selection for Propositionalization. Discovery Science 2002: 430-434 | |
40 | Susanne Hoche, Stefan Wrobel: Scaling Boosting by Margin-Based Inclusionof Features and Relations. ECML 2002: 148-160 | |
39 | Lourdes Peña Castillo, Stefan Wrobel: Macro-Operators in Multirelational Learning: A Search-Space Reduction Technique. ECML 2002: 357-368 | |
38 | Lourdes Peña Castillo, Stefan Wrobel: On the Stability of Example-Driven Learning Systems: A Case Study in Multirelational Learning. MICAI 2002: 321-330 | |
37 | Tobias Scheffer, Stefan Wrobel: A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases. PKDD 2002: 397-409 | |
36 | Tobias Scheffer, Stefan Wrobel: Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling. Journal of Machine Learning Research 3: 833-862 (2002) | |
35 | Tobias Scheffer, Stefan Wrobel, Borislav Popov, Damyan Ognianov, Christian Decomain, Susanne Hoche: Lerning Hidden Markov Models for Information Extraction Actively from Partially Labeled Text. KI 16(2): 17-22 (2002) | |
2001 | ||
34 | Tamás Horváth, Stefan Wrobel: Towards Discovery of Deep and Wide First-Order Structures: A Case Study in the Domain of Mutagenicity. Discovery Science 2001: 100-112 | |
33 | Stefan Wrobel: Scalability, Search, and Sampling: From Smart Algorithms to Active Discovery. ECML 2001: 615 | |
32 | Tobias Scheffer, Christian Decomain, Stefan Wrobel: Mining the Web with Active Hidden Markov Models. ICDM 2001: 645-646 | |
31 | Tobias Scheffer, Stefan Wrobel: Incremental Maximization of Non-Instance-Averaging Utility Functions with Applications to Knowledge Discovery Problems. ICML 2001: 481-488 | |
30 | Tobias Scheffer, Christian Decomain, Stefan Wrobel: Active Hidden Markov Models for Information Extraction. IDA 2001: 309-318 | |
29 | Mark-A. Krogel, Stefan Wrobel: Transformation-Based Learning Using Multirelational Aggregation. ILP 2001: 142-155 | |
28 | Susanne Hoche, Stefan Wrobel: Relational Learning Using Constrained Confidence-Rated Boosting. ILP 2001: 51-64 | |
27 | Stefan Wrobel: Scalability, Search, and Sampling: From Smart Algorithms to Active Discovery. PKDD 2001: 507 | |
26 | Tamás Horváth, Stefan Wrobel, Uta Bohnebeck: Relational Instance-Based Learning with Lists and Terms. Machine Learning 43(1/2): 53-80 (2001) | |
2000 | ||
25 | Mathias Kirsten, Stefan Wrobel: Extending K-Means Clustering to First-Order Representations. ILP 2000: 112-129 | |
24 | Tobias Scheffer, Stefan Wrobel: A sequential sampling algorithm for a general class of utility criteria. KDD 2000: 330-334 | |
1999 | ||
23 | Tamás Horváth, Zoltán Alexin, Tibor Gyimóthy, Stefan Wrobel: Application of Different Learning Methods to Hungarian Part-of-Speech Tagging. ILP 1999: 128-139 | |
1998 | ||
22 | Stefan Wrobel: Scalability Issues in Inductive Logic Programming. ALT 1998: 11-30 | |
21 | Uta Bohnebeck, Werner Sälter, Tamás Horváth, Stefan Wrobel, Dietmar Blohm: Measuring similarity of RNA structures by relational instance-based learning: A first step toward detecting RNA signal structures in silico. German Conference on Bioinformatics 1998 | |
20 | Mathias Kirsten, Stefan Wrobel: Relational Distance-Based Clustering. ILP 1998: 261-270 | |
19 | Uta Bohnebeck, Tamás Horváth, Stefan Wrobel: Term Comparisons in First-Order Similarity Measures. ILP 1998: 65-79 | |
18 | Stefan Wrobel: Data Mining Serviceteil. KI 12(1): 58-59 (1998) | |
17 | Stefan Wrobel: Data Mining und Wissensentdeckung in Datenbanken. KI 12(1): 6-10 (1998) | |
16 | Mathias Kirsten, Stefan Wrobel, F. Wilhelm Dahmen, Hans-Christoph Dahmen: Einsatz von Data Mining-Techniken zur Analyse ökologischer Standort- und Pflanzendaten. KI 12(2): 39-42 (1998) | |
1997 | ||
15 | Stefan Wrobel: An Algorithm for Multi-relational Discovery of Subgroups. PKDD 1997: 78-87 | |
14 | Werner Emde, Jörg Rahmer, Angi Voß, Christian Beilken, Josef Börding, Wolfgang Orth, Ulrike Petersen, Jörg Walter Schaaf, Michael Spenke, Stefan Wrobel: Interactive Configuration in KIKon. XPS 1997: 79-91 | |
13 | Stefan Wrobel: Data Mining - Das aktuelle Schlagwort. KI 11(1): 22 (1997) | |
1996 | ||
12 | Stefan Wrobel, Dietrich Wettschereck, Edgar Sommer, Werner Emde: Extensibility in Data Mining Systems. KDD 1996: 214-219 | |
11 | Nada Lavrac, Stefan Wrobel: Induktive Logikprogrammierung - Grundlagen und Techniken. KI 10(3): 46-54 (1996) | |
1995 | ||
10 | Nada Lavrac, Stefan Wrobel: Machine Learning: ECML-95, 8th European Conference on Machine Learning, Heraclion, Crete, Greece, April 25-27, 1995, Proceedings Springer 1995 | |
1994 | ||
9 | Stefan Wrobel: Concept Formation During Interactive Theory Revision. Machine Learning 14(1): 169-191 (1994) | |
1993 | ||
8 | Stefan Wrobel: On the Proper Definition of Minimality in Specialization and Theory Revision. ECML 1993: 65-82 | |
1991 | ||
7 | Francesco Bergadano, Floriana Esposito, Céline Rouveirol, Stefan Wrobel: Panel: Evaluating and Changing Representation in Concept Acquisition. EWSL 1991: 89-100 | |
6 | Stefan Wrobel: Towards a Model of Grounded Concept Formation. IJCAI 1991: 712-719 | |
5 | Stefan Wrobel: Die Umweltverankerung von Begriffsbildungsprozessen. KI 5(1): 22-26 (1991) | |
1988 | ||
4 | Stefan Wrobel: Automatic Representation Adjustment in an Observational Discovery System. EWSL 1988: 253-262 | |
3 | Stefan Wrobel: Design Goals for Sloppy Modeling Systems. International Journal of Man-Machine Studies 29(4): 461-477 (1988) | |
1987 | ||
2 | Stefan Wrobel: Higher-order Concepts in a Tractable Knowledge Representation. GWAI 1987: 129-138 | |
1 | Stefan Wrobel: Demand-Driven Concept Formation. Knowledge Representation and Organization in Machine Learning 1987: 289-319 |