Daniel Horn
Daniel Horn
Statistical Methods for Big Data, TU Dortmund
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Zitiert von
Zitiert von
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
BBmisc: Miscellaneous helper functions for B. Bischl
B Bischl, M Lang, J Bossek, D Horn, J Richter, D Surmann
R package version 1.11, 2017
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, 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
Industrial data science: developing a qualification concept for machine learning in industrial production
N Bauer, L Stankiewicz, M Jastrow, D Horn, J Teubner, K Kersting, ...
European Conference on Data Analysis (ECDA), 2018
Efficient global optimization: Motivation, variations and applications
C Weihs, S Herbrandt, N Bauer, K Friedrichs, D Horn
A first analysis of kernels for kriging-based optimization in hierarchical search spaces
M Zaefferer, D Horn
Parallel Problem Solving from Nature–PPSN XV: 15th International Conference …, 2018
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
ParamHelpers: Helpers for parameters in black-box optimization, tuning, and machine learning
B Bischl, M Lang, J Bossek, D Horn, K Schork, J Richter, P Kerschke
R package version 1, 23, 2016
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
Fast model selection by limiting SVM training times
A Demircioglu, D Horn, T Glasmachers, B Bischl, C Weihs
arXiv preprint arXiv:1602.03368, 2016
Multi-objective selection of algorithm portfolios: Experimental validation
D Horn, K Schork, T Wagner
Parallel Problem Solving from Nature–PPSN XIV: 14th International Conference …, 2016
Big Data Classification-Many Features, Many Observations
C Weihs, D Horn, B Bischl
Using Sequential Statistical Tests to Improve the Performance of Random Search in hyperparameter Tuning
P Buczak, D Horn
arXiv preprint arXiv:2112.12438, 2021
Multi-objective Analysis of Machine Learning Algorithms Using Model-based Optimization Techniques
RODD: Robust Outlier Detection in Data Cubes
L Kuhlmann, D Wilmes, E Müller, M Pauly, D Horn
arXiv preprint arXiv:2303.08193, 2023
Random boosting and random^ 2 forests--A random tree depth injection approach
TM Krabel, TNT Tran, A Groll, D Horn, C Jentsch
arXiv preprint arXiv:2009.06078, 2020
B: A Comparative Study on Kernelized Support Vector Machines
D Horn, A Demircioglu, B Bischl, T Glasmachers, C Weihs
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