Data Mining and Knowledge Discovery

, Volume 19, Issue 2, pp 173–175 | Cite as

Guest editors’ introduction: special issue of selected papers from ECML PKDD 2009

  • Aleksander Kolcz
  • Dunja Mladenic
  • Wray Buntine
  • Marko Grobelnik
  • John Shawe-Taylor
Article

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References

  1. Akoglu L, Faloutsos C (2009) RTG: a recursive realistic graph generator using random typing. Data Min Knowl Discov. doi: 10.1007/s10618-009-0140-7
  2. Bonchi F, Castillo C, Donato D (2009) Taxonomy-driven lumping for sequence mining. Data Min Knowl Discov. doi: 10.1007/s10618-009-0141-6
  3. Cheng W, Hüellermeier E (2009) Combining instance-based learning and logistic regression for multilabel classification. Mach Learn. doi: 10.1007/s10994-009-5127-5
  4. Gärtner T, Vembu S (2009) On structured output training: hard cases and an efficient alternative. Mach Learn. doi: 10.1007/s10994-009-5129-3
  5. Grosskreutz H, Rüping S (2009) On subgroup discovery in numerical domains. Data Min Knowl Discov. doi: 10.1007/s10618-009-0136-3
  6. Huopaniemi I, Suvitaival T, Nikkila J, Oresic M, Kaski S (2009) Two-way analysis of high-dimensional collinear data. Data Min Knowl Discov. doi: 10.1007/s10618-009-0142-5
  7. Joachims T, Yu CNJ (2009) Sparse kernel SVMs via cutting plane training. Mach Learn. doi: 10.1007/s10994-009-5126-6
  8. Johns J, Petrik M, Mahadevan S (2009) Hybrid least-squares algorithms for approximate policy evaluation. Mach Learn. doi: 10.1007/s10994-009-5128-4
  9. Kranen P, Seidl T (2009) Harnessing the strengths of anytime algorithms for constant data streams. Data Min Knowl Discov. doi: 10.1007/s10618-009-0139-0
  10. Liu A, Jun G, Ghosh J (2009) A self-training approach to cost sensitive uncertainty sampling. Mach Learn. doi: 10.1007/s10994-009-5131-9
  11. Roth D, Samdani R (2009) Learning multi-linear representations of distributions for efficient inference. Mach Learn 1(1):. doi: 10.1007/s10994-009-5130-x
  12. Santos-Rodríguez R, Alaiz-Rodríguez R, Guerrero-Curieses A, Cid-Sueiro J (2009) Cost-sensitive learning based on Bregman divergences. Mach Learn. doi: 10.1007/s10994-009-5132-8
  13. Leeuwen M, Vreeken J, Siebes A (2009) Identifying the components. Data Min Knowl Discov. doi: 10.1007/s10618-009-0137-2
  14. Zhao Q-L, Jiang Y-H, Xu M (2009) A fast ensemble pruning algorithm based on pattern mining process. Data Min Knowl Discov. doi: 10.1007/s10618-009-0138-1

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Aleksander Kolcz
    • 1
  • Dunja Mladenic
    • 2
  • Wray Buntine
    • 3
    • 4
  • Marko Grobelnik
    • 2
  • John Shawe-Taylor
    • 5
  1. 1.MicrosoftRedmondUSA
  2. 2.Jozef Stefan InstituteLjubljanaSlovenia
  3. 3.Helsinki Institute of ITHelsinkiFinland
  4. 4.NICTASydneyAustralia
  5. 5.University College LondonLondonUK

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