J. Lin |
Machine Learning pipeline search with bayesian optimization and reinforcement learning |
MA |
2019 |
M. Graber |
Localizing phosphorylation sites by deep learning-based fragment ion intensity |
|
|
prediction |
MA |
2018 |
|
J. Moosbauer |
Bayesian Optimization under Noise for Model Selection in Machine Learning |
MA |
2018 |
J. Fried |
Interpretable Machine Learning - An Application Study using the Munich Rent Index |
MA |
2018 |
J. Goschenhofer |
Wearable-based Parkinson’s Disease Severity Monitoring using Deep Learning |
MA |
2018 |
S. Gruber |
Visualization and Efficient Replay Memory for Reinforcement Learning |
BA |
2018 |
S. Coors |
Automatic Gradient Boosting |
MA |
2018 |
D. Schalk |
Efficient and Distributed Model-Based Boosting for Large Datasets |
MA |
2018 |
K. Engelhardt |
Linear individual model-agnostic explanations - discussion and empirical analysis of modifications |
MA |
2018 |
N. Klein |
Extending Hyperband with Model-Based Sampling Strategies |
MA |
2018 |
M. Dumke |
Reinforcement learning in R |
MA |
2018 |
M. Lee |
Anomaly Detection using Machine Learning Methods |
MA |
2018 |
J. Langer |
RNN Bandmatrix |
MA |
2018 |
B. Klepper |
Configuration of deep neural networks using model-based optimization |
MA |
2017 |
F. Pfisterer |
Kernelized anomaly detection |
MA |
2017 |
M. Binder |
Automatic model selection amd hyperparameter optimization |
MA |
2017 |
V. Mayer |
mlrMBO / RF distance based infill criteria |
MA |
2017 |
L. Haller |
Kostensensitive Entscheidungsbäume für beobachtungsabhängige Kosten |
BA |
2016 |
B. Zhang |
Implementation of 3D Model Visualization for Machine Learning |
BA |
2016 |
T. Riebe |
Eine Simulationsstudie zum Sampled Boosting |
BA |
2016 |
P. Rösch |
Implementation and Comparison of Stacking Methods for Machine Learning |
MA |
2016 |
M. Erdmann |
Runtime estimation of ML models |
BA |
2016 |
A.Exterkate |
Process Mining: Checking Methods for Process Conformance |
MA |
2016 |
J.-Q. Au |
Implementation of Multilabel Algorithms and their Application on Driving Data |
MA |
2016 |
|
(J.-Q. Au was a master student of TU Dortmund) |
|
|
J. Thomas |
Stability Selection for Component-Wise Gradient Boosting in Multiple Dimensions |
MA |
2016 |
A. Franz |
Detecting Future Equipment Failures: Predictive Maintenance in Chemical Industrial Plants |
MA |
2016 |
T. Kühn |
Fault Detection for Fire Alarm Systems based on Sensor Data |
MA |
2016 |
B. Schober |
Laufzeitanalyse von Klassifikationsverfahren in R |
BA |
2015 |
F. Pfisterer |
Benchmark Analysis for Machine Learning in R |
BA |
2015 |
T. Kühn |
Implementierung und Evaluation ergänzender Korrekturmethoden für statistische Lernverfahren |
BA |
2014 |
|
bei unbalancierten Klassifikationsproblemen |
|
|