Draft:Andreas Christmann
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Comment: Two paragraphs are unsourced. More than half the article. This is not good for a biography. 🍗TheNuggeteer🍗 (My "blotter")05:22, 20 July 2025 (UTC)
Andreas Christmann (* 1963) is a German mathematician. His main research topics are
Statistics, Statistical learning theory, and Robust statistics.
Christmann studied statistics with biology as his secondary topic at University of Dortmund (renamed in 2007). After positions as a visiting professor at KU Leuven in Belgium and as Professor at University of Dortmund in Germany and Vrije Universiteit Brussel in Belgium, he got a position as full professor (chair) of "Stochastics and Machine Learning" at the University of Bayreuth."Prof. Dr. Andreas Christmann". University of Bayreuth. Retrieved July 17, 2025.
He was one of the organizers of the Oberwolfach workshop "Learning Theory and Approximation" (in 2016), together with Prof. Dr. Steve Smale, Prof. Dr. Ding-Xuan Zhou, and Prof. Dr. Kurt Jetter."Learning Theory and Approximation". Mathematisches Forschungsinstitut Oberwolfach. Retrieved July 17, 2025. Between 2013 and 2019, he served as Action Editor of Journal of Machine Learning Research, which is a top-tier open-access journal in machine learning (often ranked among the best in AI/stats).
With Prof. Dr. Ingo Steinwart, he co-authored a textbook on Support vector machine.[1] The book provides a rigorous mathematical treatment of support vector machines and their robustness properties. In a review for Mathematical Reviews, Gilles Blanchard praised it as "probably the first book on this topic (Vapnik's original work aside) which is genuinely aimed at a mathematician reader" and predicted that it "is bound to be recognized as a classic reference on this topic."[2] Adriana Horníková, in a Technometrics review, described it as a "athematically elaborated" work, rich with definitions and examples, and noted its suitability as a textbook for graduate courses due to its comprehensive structure, including 12 chapters and 9 appendices covering marginal SVM applications.[3] This book has been widely cited (over 5,000 citations as of July 2025), alongside Christmann's papers on SVM robustness, which collectively contribute to his over 8,500 citations and an h-index of 29. [4] [5] [6] [7]
External links
[edit]- Andreas Christmann's profile at Google Scholar
- Andreas Christmann's profile at University of Bayreuth
- Andreas Christmann's profile at ResearchGate
- Andreas Christmann's profile at DBLP
References
[edit]- ^ Steinwart, Ingo; Christmann, Andreas (2008). Support Vector Machines. Springer. doi:10.1007/978-0-387-77242-4. ISBN 978-0-387-77242-4.
- ^ Blanchard, Gilles (2010). ">https://mathscinet.ams.org/mathscinet/article?mr=2450103 "Review of Support Vector Machines by Ingo Steinwart and Andreas Christmann". Mathematical Reviews (f). MR 2450103. Retrieved July 17, 2025.
- ^ Horníková, Adriana (2011). "Review of Support Vector Machines by Ingo Steinwart and Andreas Christmann". Technometrics. 53 (2): 210. doi:10.1198/TECH.2011.BR532.
- ^ Xu, Huan; Caramanis, Constantine; Mannor, Shie (2009). "Robustness and regularization of support vector machines". Journal of Machine Learning Research. 10 (7): 1485–1510.
- ^ Fukumizu, Kenji; Gretton, Arthur; Lanckriet, Gert; Schölkopf, Bernhard; Sriperumbudur, Bharath K (2009). Y. Bengio, D. Schuurmans, J. Lafferty, C. Williams, A. Culotta (ed.). Advances in Neural Information Processing Systems (PDF). Vol. 22. Curran Associates, Inc.
{{cite conference}}: CS1 maint: multiple names: editors list (link) - ^ Meister, Mona; Steinwart, Ingo (2016). "Optimal learning rates for localized SVMs". Journal of Machine Learning Research. 17 (194): 1–44.
- ^ Deniz, Ayça; Kiziloz, Hakan Ezgi; Dokeroglu, Tansel; Cosar, Ahmet (2017). "Robust multiobjective evolutionary feature subset selection algorithm for binary classification using machine learning techniques". Neurocomputing. 241. Elsevier: 128–146.