Stefan Kramer, an Austrian computer scientist and since 2011 professor at University of Mainz, before at Technical University of Munich. He holds a Ph.D. on machine learning from Vienna University of Technology, where he also worked with Bernhard Pfahringer, Johannes Fürnkranz and Hermann Kaindl. His research interests include bioinformatics, cheminformatics, predictive toxicology, toxicogenomics, pharmacogenomics, data mining, relational data mining, inductive logic programming, inductive databases [1], and software engineering.
Stefan Kramer [2]

Selected Publications

1995 ...

• Bernhard Pfahringer, Stefan Kramer (1995). Compression-Based Evaluation of Partial Determinations. KDD-95, AAAI Press, pdf ^[4]
• Stefan Kramer (1996). Structural Regression Trees. AAAI/IAAI, Vol. 1 1996, pdf

2000 ...

• Stefan Kramer (2000). Relational learning vs. propositionalization. Ph.D. thesis, ps, also in AI Communications, Vol. 13, No. 4, draft as ps
• Johannes Fürnkranz, Bernhard Pfahringer, Hermann Kaindl, Stefan Kramer (2000). Learning to Use Operational Advice. ECAI-00, pdf
• Stefan Kramer (2001). Demand-Driven Construction of Structural Features in ILP. ILP-2001
• Stefan Kramer, Luc De Raedt, Christoph Helma (2001). Molecular Feature Mining in HIV Data. KDD-2001, draft as pdf
• Hermann Kaindl, Stefan Kramer (2006). Are Automated Traceability Checks Needed? ISOneWorld 2006
• Ulrich Rückert, Stefan Kramer (2007). Optimizing feature sets for structured data. ECML 2007, draft as pdf

2010 ...

• Pawel Smialowski, Dmitrij Frishman, Stefan Kramer (2010). Pitfalls of supervised feature selection. Bioinformatics, Vol. 26, No. 3

External Links

• Stefan Kramer | LinkedIn
• Stefan Kramer - Google Scholar Citations

References

1. ^ Backward induction from Wikipedia
2. ^ Stefan Kramer - Google Scholar Citations
3. ^ http://dblp.uni-trier.de/pers/hd/k/Kramer_0001:Stefan|DBLP: Stefan Kramer 0001]]
4. ^ Johannes Fürnkranz (1997). Knowledge Discovery in Chess Databases: A Research Proposal. Technical Report OEFAI-TR-97-33, Austrian Research Institute for Artificial Intelligence, zipped ps, pdf
   3.6 Non-classification techniques: ... General dependencies [Pfahringer and Kramer, 1995] might find interesting applications in chess databases for discovering typical piece-patterns, such as “In many cases when white castles queen-sides, he will sooner or later play h4.”

What links here?