Daniel Horn
Daniel Horn
Computational Statistics, TU Dortmund
Verified email at tu-dortmund.de - Homepage
Cited by
Cited by
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
B Bischl, J Richter, J Bossek, D Horn, J Thomas, M Lang
arXiv preprint arXiv:1703.03373, 2017
Model-based multi-objective optimization: taxonomy, multi-point proposal, toolbox and benchmark
D Horn, T Wagner, D Biermann, C Weihs, B Bischl
International Conference on Evolutionary Multi-Criterion Optimization, 64-78, 2015
Multi-objective parameter configuration of machine learning algorithms using model-based optimization
D Horn, B Bischl
Computational Intelligence (SSCI), 2016 IEEE Symposium Series on, 1-8, 2016
A comparative study on large scale kernelized support vector machines
D Horn, A Demircioğlu, B Bischl, T Glasmachers, C Weihs
Advances in Data Analysis and Classification 12 (4), 867-883, 2018
First Investigations on Noisy Model-Based Multi-objective Optimization
D Horn, M Dagge, X Sun, B Bischl
International Conference on Evolutionary Multi-Criterion Optimization, 298-313, 2017
Efficient global optimization: Motivation, variations and applications
C Weihs, S Herbrandt, N Bauer, K Friedrichs, D Horn
BBmisc: Miscellaneous Helper Functions for B
B Bischl, M Lang, J Bossek, D Horn, J Richter, D Surmann
Bischl. R package version 1, 2017
A First Analysis of Kernels for Kriging-Based Optimization in Hierarchical Search Spaces
M Zaefferer, D Horn
International Conference on Parallel Problem Solving from Nature, 399-410, 2018
Multi-objective Selection of Algorithm Portfolios: Experimental Validation
D Horn, K Schork, T Wagner
International Conference on Parallel Problem Solving from Nature, 421-430, 2016
Big Data Classification-Many Features, Many Observations
C Weihs, D Horn, B Bischl
Multi-objective selection of algorithm portfolios
D Horn, B Bischl, A Demircioglu, T Glasmachers, T Wagner, C Weihs
Archives of Data Science, Series A (Online First) 2 (1), 15s, 2017
Surrogates for hierarchical search spaces: the wedge-kernel and an automated analysis
D Horn, J Stork, NJ Schüßler, M Zaefferer
Proceedings of the Genetic and Evolutionary Computation Conference, 916-924, 2019
Multi-objective Analysis of Machine Learning Algorithms Using Model-based Optimization Techniques
Fast model selection by limiting SVM training times
A Demircioglu, D Horn, T Glasmachers, B Bischl, C Weihs
arXiv preprint arXiv:1602.03368, 2016
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