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Robust learning from bites for data mining

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  • Christmann, Andreas
  • Steinwart, Ingo
  • Hubert, Mia

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  • Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2007. "Robust learning from bites for data mining," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 347-361, September.
    • Handle: RePEc:eee:csdana:v:52:y:2007:i:1:p:347-361
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    References listed on IDEAS

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    1. Andreas Christmann & Paul Fischer & Thorsten Joachims, 2002. "Comparison between various regression depth methods and the support vector machine to approximate the minimum number of misclassifications," Computational Statistics, Springer, vol. 17(2), pages 273-287, July.
    2. Christmann, Andreas & Steinwart, Ingo, 2005. "Consistency and robustness of kernel based regression," Technical Reports 2005,01, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    3. Willems, Gert & Van Aelst, Stefan, 2005. "Fast and robust bootstrap for LTS," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 703-715, April.
    4. Christophe Croux & Stefan Aelst & Catherine Dehon, 2003. "Bounded influence regression using high breakdown scatter matrices," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(2), pages 265-285, June.
    5. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, December.
    6. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    7. Bartlett, Peter L. & Jordan, Michael I. & McAuliffe, Jon D., 2006. "Convexity, Classification, and Risk Bounds," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 138-156, March.
    8. Van Aelst, Stefan & Rousseeuw, Peter J. & Hubert, Mia & Struyf, Anja, 2002. "The Deepest Regression Method," Journal of Multivariate Analysis, Elsevier, vol. 81(1), pages 138-166, April.
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    2. Granell, Ramon & Axon, Colin J. & Wallom, David C.H., 2014. "Predicting winning and losing businesses when changing electricity tariffs," Applied Energy, Elsevier, vol. 133(C), pages 298-307.
    3. Rokach, Lior, 2009. "Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4046-4072, October.

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