Author of the publication

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Universal kernels on non-standard input spaces

A. Christmann, and I. Steinwart. Advances in neural information processing systems 23 : 24th Annual Conference on Neural Information Processing Systems 2010, 1, page 406-414. Red Hook, NY, Curran, (2011)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Univ. -Prof. Dr. Ingo Steinwart University of Stuttgart

I. Steinwart

Ingo Tischler University of Stuttgart

Ingo Dalcolmo University of Stuttgart

PD Dr. Ingo Hartenbach University of Stuttgart

Other publications of authors with the same name

Best-scored Random Forest Classification.H. Hang, X. Liu, and I. Steinwart. CoRR, (2019)A Framework and Benchmark for Deep Batch Active Learning for RegressionD. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. J. Mach. Learn. Res., 24 (164): 1–81 (2023)Representation of quasi-monotone functionals by families of separating hyperplanesI. Steinwart. Mathematische Nachrichten, 290 (11/12): 1859-1883 (2017)Which Minimizer Does My Neural Network Converge To?M. Nonnenmacher, D. Reeb, and I. Steinwart. Machine Learning and Knowledge Discovery in Databases : Research Track, volume 3 of Lecture Notes in Computer Science, page 87-102. Berlin, Springer, (2021)Optimal regression rates for SVMs using Gaussian kernelsM. Eberts, and I. Steinwart. Electronic journal of statistics, (2013)Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian MomentsV. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner. Software, (2021)Related to: V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,” J. Chem. Theory Comput. 17, 6658–6670 (2021). doi: 10.1021/acs.jctc.1c00527.Fast learning from α-mixing observationsH. Hang, and I. Steinwart. Journal of multivariate analysis, (2014)Oracle inequalities for support vector machines that are based on random entropy numbersI. Steinwart. Journal of complexity, 25 (5): 437-454 (2009)QP algorithms with guaranteed accuracy and run time for support vector machineD. Hush, P. Kelly, C. Scovel, and I. Steinwart. Journal of machine learning research, 7 (May): 733-769 (2006)On robustness properties of convex risk minimization methods for pattern recognitionA. Christmann, and I. Steinwart. Journal of machine learning research, 5 (August): 1007-1034 (2004)