Follow
Daniel Trabold
Daniel Trabold
Verified email at iais.fraunhofer.de
Title
Cited by
Cited by
Year
A relevance criterion for sequential patterns
H Grosskreutz, B Lang, D Trabold
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013
182013
Multilingual knowledge-based concept recognition in textual data
M Schierle, D Trabold
Advances in Data Analysis, Data Handling and Business Intelligence …, 2010
172010
Comparison of structured vs. unstructured data for industrial quality analysis
C Hänig, M Schierle, D Trabold
Proceedings of the World Congress on Engineering and Computer Science 1, 20-22, 2010
142010
Simple recurrent neural networks for support vector machine training
R Sifa, D Paurat, D Trabold, C Bauckhage
Artificial Neural Networks and Machine Learning–ICANN 2018: 27th …, 2018
112018
Mining strongly closed itemsets from data streams
D Trabold, T Horváth
Discovery Science: 20th International Conference, DS 2017, Kyoto, Japan …, 2017
102017
Extraction of failure graphs from structured and unstructured data
M Schierle, D Trabold
2008 Seventh International Conference on Machine Learning and Applications …, 2008
82008
Parallel subgroup discovery on computing clusters—first results
D Trabold, H Grosskreutz
2013 IEEE International Conference on Big Data, 575-579, 2013
62013
Mining data streams with dynamic confidence intervals
D Trabold, T Horváth
Big Data Analytics and Knowledge Discovery: 18th International Conference …, 2016
32016
The dicode data mining services
N Friesen, M Jakob, J Kindermann, D Maassen, A Poigné, S Rüping, ...
Mastering Data-Intensive Collaboration and Decision Making: Research and …, 2014
32014
Benefits of Unstructured Data for Industrial Quality Analysis
C Hänig, M Schierle, D Trabold
Intelligent Automation and Systems Engineering, 257-270, 2011
22011
Effective approximation of parametrized closure systems over transactional data streams
D Trabold, T Horváth, S Wrobel
Machine Learning 109, 1147-1177, 2020
12020
Mining Frequent Itemsets from Transactional Data Streams with Probabilistic Error Bounds
D Trabold
Rheinische Friedrich-Wilhelms-Universität Bonn, 2020
12020
Quantum Machine Learning. Eine Analyse zu Kompetenz, Forschung und Anwendung
C Bauckhage, E Brito, I Daase, L Franken, B Georgiev, D Hecker, ...
Fraunhofer IAIS, 2020
2020
In-stream frequent itemset mining with output proportional memory footprint
D Trabold, M Boley, M Mock, T Horváth
Workshop on Knowledge Discovery, Data Mining and Machine Learning 2015, 93-104, 2015
2015
Model Management für Schema-Evolution
D Trabold
The system can't perform the operation now. Try again later.
Articles 1–15