Bernard Haasdonk
Bernard Haasdonk
Professor for Numerical Mathematics
Verified email at - Homepage
Cited by
Cited by
Online handwriting recognition with support vector machines-a kernel approach
C Bahlmann, B Haasdonk, H Burkhardt
Proceedings Eighth International Workshop on Frontiers in Handwriting …, 2002
Reduced basis method for finite volume approximations of parametrized linear evolution equations
B Haasdonk, M Ohlberger
ESAIM: Mathematical Modelling and Numerical Analysis-Modélisation …, 2008
Feature space interpretation of SVMs with indefinite kernels
B Haasdonk
IEEE Transactions on pattern analysis and machine intelligence 27 (4), 482-492, 2005
Reduced basis approximation for nonlinear parametrized evolution equations based on empirical operator interpolation
M Drohmann, B Haasdonk, M Ohlberger
SIAM Journal on Scientific Computing 34 (2), A937-A969, 2012
Learning with distance substitution kernels
B Haasdonk, C Bahlmann
Joint pattern recognition symposium, 220-227, 2004
A training set and multiple bases generation approach for parameterized model reduction based on adaptive grids in parameter space
B Haasdonk, M Dihlmann, M Ohlberger
Mathematical and Computer Modelling of Dynamical Systems 17 (4), 423-442, 2011
Tangent distance kernels for support vector machines
B Haasdonk, D Keysers
Object recognition supported by user interaction for service robots 2, 864-868, 2002
Kernel discriminant analysis for positive definite and indefinite kernels
E Pȩkalska, B Haasdonk
IEEE transactions on pattern analysis and machine intelligence 31 (6), 1017-1032, 2008
Convergence rates of the pod–greedy method
B Haasdonk
ESAIM: Mathematical Modelling and Numerical Analysis 47 (3), 859-873, 2013
Efficient reduced models and a posteriori error estimation for parametrized dynamical systems by offline/online decomposition
B Haasdonk, M Ohlberger
Mathematical and Computer Modelling of Dynamical Systems 17 (2), 145-161, 2011
A posteriori error estimation for DEIM reduced nonlinear dynamical systems
D Wirtz, DC Sorensen, B Haasdonk
SIAM Journal on Scientific Computing 36 (2), A311-A338, 2014
Reduced basis methods for parametrized PDEs–a tutorial introduction for stationary and instationary problems
B Haasdonk
Model reduction and approximation: theory and algorithms 15, 65, 2017
Invariant kernel functions for pattern analysis and machine learning
B Haasdonk, H Burkhardt
Machine learning 68 (1), 35-61, 2007
A reduced basis method for evolution schemes with parameter-dependent explicit operators
B Haasdonk, M Ohlberger, G Rozza
ETNA, Electronic Transactions on Numerical Analysis 32 (ARTICLE), 145-168, 2008
Model reduction of parametrized evolution problems using the reduced basis method with adaptive time-partitioning
M Dihlmann, M Drohmann, B Haasdonk
Proc. of ADMOS 2011, 64, 2011
Certified PDE-constrained parameter optimization using reduced basis surrogate models for evolution problems
MA Dihlmann, B Haasdonk
Computational Optimization and Applications 60 (3), 753-787, 2015
The localized reduced basis multiscale method
F Albrecht, B Haasdonk, S Kaulmann, M Ohlberger
A reduced basis method for parametrized variational inequalities
B Haasdonk, J Salomon, B Wohlmuth
SIAM Journal on Numerical Analysis 50 (5), 2656-2676, 2012
Adaptive basis enrichment for the reduced basis method applied to finite volume schemes
B Haasdonk, M Ohlberger
Proc. 5th International Symposium on Finite Volumes for Complex Applications …, 2008
Reduced basis methods for parameterized partial differential equations with stochastic influences using the Karhunen--Loève expansion
B Haasdonk, K Urban, B Wieland
SIAM/ASA Journal on Uncertainty Quantification 1 (1), 79-105, 2013
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