Tomáš Kliegr
Geciteerd door
Geciteerd door
Query refinement and user relevance feedback for contextualized image retrieval
K Chandramouli, T Kliegr, J Nemrava, V Svátek, E Izquierdo
IET Digital Library, 2008
A brief overview of rule learning
J Fürnkranz, T Kliegr
International symposium on rules and rule markup languages for the semantic …, 2015
Entityclassifier. eu: real-time classification of entities in text with Wikipedia
M Dojchinovski, T Kliegr
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2013
Combining image captions and visual analysis for image concept classification
T Kliegr, K Chandramouli, J Nemrava, V Svatek, E Izquierdo
Proceedings of the 9th International Workshop on Multimedia Data Mining …, 2008
Linked hypernyms: Enriching dbpedia with targeted hypernym discovery
T Kliegr
Journal of Web Semantics 31, 59-69, 2015
On cognitive preferences and the plausibility of rule-based models
J Fürnkranz, T Kliegr, H Paulheim
Machine Learning 109 (4), 853-898, 2020
Learning business rules with association rule classifiers
T Kliegr, J Kuchař, D Sottara, S Vojíř
International Symposium on Rules and Rule Markup Languages for the Semantic …, 2014
LHD 2.0: A text mining approach to typing entities in knowledge graphs
T Kliegr, O Zamazal
Journal of Web Semantics 39, 47-61, 2016
Semantic analytical reports: A framework for post-processing data mining results
T Kliegr, M Ralbovský, V Svátek, M Šimůnek, V Jirkovský, J Nemrava, ...
International Symposium on Methodologies for Intelligent Systems, 88-98, 2009
A review of possible effects of cognitive biases on interpretation of rule-based machine learning models
T Kliegr, Š Bahník, J Fürnkranz
arXiv preprint arXiv:1804.02969, 2018
EasyMiner. eu: Web framework for interpretable machine learning based on rules and frequent itemsets
S Vojíř, V Zeman, J Kuchař, T Kliegr
Knowledge-Based Systems 150, 111-115, 2018
UTA-NM: Explaining stated preferences with additive non-monotonic utility functions
T Kliegr
Preference Learning, 56, 2009
Benchmark of rule-based classifiers in the news recommendation task
T Kliegr, J Kuchař
International Conference of the Cross-Language Evaluation Forum for European …, 2015
SEWEBAR-CMS: semantic analytical report authoring for data mining results
T Kliegr, V Svátek, M Ralbovský, M Šimůnek
Journal of Intelligent Information Systems 37 (3), 371-395, 2011
Towards Linked Hypernyms Dataset 2.0: complementing DBpedia with hypernym discovery and statistical type inferrence
T Kliegr, O Zamazal
Proceedings of The Ninth International Conference on Language Resources and …, 2014
Association rule mining following the web search paradigm
R Škrabal, M Šimůnek, S Vojíř, A Hazucha, T Marek, D Chudán, T Kliegr
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2012
Entity classification by bag of Wikipedia articles
T Kliegr
Proceedings of the 3rd workshop on Ph. D. students in information and …, 2010
An XML format for association rule models based on the GUHA method
T Kliegr, J Rauch
International Workshop on Rules and Rule Markup Languages for the Semantic …, 2010
Crowdsourced corpus with entity salience annotations
M Dojchinovski, D Reddy, T Kliegr, T Vitvar, H Sack
Proceedings of the Tenth International Conference on Language Resources and …, 2016
Transforming association rules to business rules: EasyMiner meets Drools
S Vojır, T Kliegr, A Hazucha, R Škrabal, M Šimunek
RuleML-2013 Challenge. CEUR-WS. org 49, 2013
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