Bernd Bischl spends much of his time researching Artificial intelligence, Machine learning, Data mining, Benchmark and Optimization problem. His work often combines Artificial intelligence and Citizen science studies. His work on Hyperparameter and Model selection is typically connected to Work and Structure as part of general Machine learning study, connecting several disciplines of science.
His Hyperparameter study incorporates themes from Hyperparameter optimization, Visualization and Cluster analysis. Bernd Bischl combines subjects such as Toolbox, Kriging and Multi-objective optimization with his study of Data mining. His research in Benchmark intersects with topics in Black box, Bayesian optimization, Categorical variable, Modular design and Feature selection.
Bernd Bischl focuses on Artificial intelligence, Machine learning, R package, Feature selection and Data mining. His Artificial intelligence research includes themes of Set and Pattern recognition. His work carried out in the field of Machine learning brings together such families of science as Set and Benchmark.
Many of his R package research pursuits overlap with Computational science, Work, Black box, Download and Interface. His Feature selection study integrates concerns from other disciplines, such as Algorithm and Boosting. The various areas that Bernd Bischl examines in his Data mining study include Statistical classification and Clustering high-dimensional data.
His primary scientific interests are in R package, Artificial intelligence, Machine learning, Computational science and Feature selection. His research on Artificial intelligence frequently links to adjacent areas such as Survival analysis. As part of his studies on Machine learning, Bernd Bischl often connects relevant areas like Field.
His studies deal with areas such as Preprocessor and Computation as well as Computational science. His Benchmark research extends to Feature selection, which is thematically connected. His Benchmark course of study focuses on High dimensional and Data mining.
Artificial intelligence, Machine learning, R package, Interpretability and Deep learning are his primary areas of study. His Artificial intelligence research is multidisciplinary, relying on both Documentation, Survival modeling, Survival analysis, License and Pattern recognition. Bernd Bischl does research in Machine learning, focusing on Categorical variable specifically.
His studies in Interpretability integrate themes in fields like Multi-objective optimization and Feature. His Multi-objective optimization research incorporates elements of Hyperparameter, Group method of data handling, Hyperparameter optimization, Benchmark and Evolutionary algorithm. His research investigates the link between Deep learning and topics such as Regression analysis that cross with problems in Mixture model, Regression and Deep neural networks.
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OpenML: networked science in machine learning
Joaquin Vanschoren;Jan N. van Rijn;Bernd Bischl;Luis Torgo.
Sigkdd Explorations (2014)
mlr: machine learning in R
Bernd Bischl;Michel Lang;Lars Kotthoff;Julia Schiffner.
Journal of Machine Learning Research (2016)
Exploratory landscape analysis
Olaf Mersmann;Bernd Bischl;Heike Trautmann;Mike Preuss.
genetic and evolutionary computation conference (2011)
ASlib: A Benchmark Library for Algorithm Selection
Bernd Bischl;Pascal Kerschke;Lars Kotthoff;Marius Thomas Lindauer.
Artificial Intelligence (2016)
Benchmark for filter methods for feature selection in high-dimensional classification data
Andrea Bommert;Xudong Sun;Bernd Bischl;Jörg Rahnenführer.
Computational Statistics & Data Analysis (2020)
Tunability: Importance of Hyperparameters of Machine Learning Algorithms
Philipp Probst;Anne-Laure Boulesteix;Bernd Bischl.
Journal of Machine Learning Research (2019)
Resampling methods for meta-model validation with recommendations for evolutionary computation
B. Bischl;O. Mersmann;H. Trautmann;C. Weihs.
Evolutionary Computation (2012)
Algorithm selection based on exploratory landscape analysis and cost-sensitive learning
Bernd Bischl;Olaf Mersmann;Heike Trautmann;Mike Preuß.
genetic and evolutionary computation conference (2012)
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
Bernd Bischl;Jakob Richter;Jakob Bossek;Daniel Horn.
arXiv: Machine Learning (2017)
Personality traits predict smartphone usage.
Clemens Stachl;Sven Hilbert;Sven Hilbert;Jiew–Quay Au;Daniel Buschek.
European Journal of Personality (2017)
Profile was last updated on December 6th, 2021.
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