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Daniel Trabold
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2020 – today
- 2020
- [b1]Daniel Trabold:
Mining Frequent Itemsets from Transactional Data Streams with Probabilistic Error Bounds. University of Bonn, Germany, 2020 - [j1]Daniel Trabold, Tamás Horváth, Stefan Wrobel:
Effective approximation of parametrized closure systems over transactional data streams. Mach. Learn. 109(6): 1147-1177 (2020) - [e1]Daniel Trabold, Pascal Welke, Nico Piatkowski:
Proceedings of the Conference "Lernen, Wissen, Daten, Analysen", Online, September 9-11, 2020. CEUR Workshop Proceedings 2738, CEUR-WS.org 2020 [contents]
2010 – 2019
- 2018
- [c8]Rafet Sifa, Daniel Paurat, Daniel Trabold, Christian Bauckhage:
Simple Recurrent Neural Networks for Support Vector Machine Training. ICANN (3) 2018: 13-22 - 2017
- [c7]Daniel Trabold, Tamás Horváth:
Mining Strongly Closed Itemsets from Data Streams. DS 2017: 251-266 - 2016
- [c6]Daniel Trabold, Tamás Horváth:
Mining Data Streams with Dynamic Confidence Intervals. DaWaK 2016: 99-113 - 2015
- [c5]Daniel Trabold, Mario Boley, Michael Mock, Tamás Horváth:
In-Stream Frequent Itemset Mining With Output Proportional Memory Footprint. LWA 2015: 93-104 - 2013
- [c4]Daniel Trabold, Henrik Grosskreutz:
Parallel subgroup discovery on computing clusters - First results. IEEE BigData 2013: 575-579 - [c3]Henrik Grosskreutz, Bastian Lang, Daniel Trabold:
A Relevance Criterion for Sequential Patterns. ECML/PKDD (1) 2013: 369-384
2000 – 2009
- 2008
- [c2]Martin Schierle, Daniel Trabold:
Multilingual Knowledge-Based Concept Recognition in Textual Data. GfKl 2008: 327-336 - [c1]Martin Schierle, Daniel Trabold:
Extraction of Failure Graphs from Structured and Unstructured Data. ICMLA 2008: 324-330
Coauthor Index
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