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Tomáš Kliegr
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A review of possible effects of cognitive biases on interpretation of rule-based machine learning models
T Kliegr, Š Bahník, J Fürnkranz
Artificial Intelligence 295, 103458, 2021
1282021
A brief overview of rule learning
J Fürnkranz, T Kliegr
International symposium on rules and rule markup languages for the semantic …, 2015
1062015
On cognitive preferences and the plausibility of rule-based models
J Fürnkranz, T Kliegr, H Paulheim
Machine Learning 109 (4), 853-898, 2020
892020
Entityclassifier. eu: real-time classification of entities in text with Wikipedia
M Dojchinovski, T Kliegr
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013
542013
Query refinement and user relevance feedback for contextualized image retrieval
K Chandramouli, T Kliegr, J Nemrava, V Svátek, E Izquierdo
2008 5th International Conference on Visual Information Engineering (VIE …, 2008
512008
Linked hypernyms: Enriching dbpedia with targeted hypernym discovery
T Kliegr
Journal of Web Semantics 31, 59-69, 2015
502015
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
462008
Learning business rules with association rule classifiers
T Kliegr, J Kuchař, D Sottara, S Vojíř
Rules on the Web. From Theory to Applications: 8th International Symposium …, 2014
392014
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
352016
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
292018
Associative Classification in R: arc, arulesCBA, and rCBA.
M Hahsler, I Johnson, T Kliegr, J Kucha
R Journal 9 (2), 2019
232019
UTA-NM: Explaining stated preferences with additive non-monotonic utility functions
T Kliegr
Preference Learning 56, 2009
232009
Semantic analytical reports: A framework for post-processing data mining results
T Kliegr, M Ralbovský, V Svátek, M Šimůnek, V Jirkovský, J Nemrava, ...
Foundations of Intelligent Systems: 18th International Symposium, ISMIS 2009 …, 2009
232009
Advances in machine learning for the behavioral sciences
T Kliegr, Š Bahník, J Fürnkranz
American Behavioral Scientist 64 (2), 145-175, 2020
202020
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
192016
Benchmark of rule-based classifiers in the news recommendation task
T Kliegr, J Kuchař
Experimental IR Meets Multilinguality, Multimodality, and Interaction: 6th …, 2015
182015
Why was this cited? Explainable machine learning applied to COVID-19 research literature
L Beranová, MP Joachimiak, T Kliegr, G Rabby, V Sklenák
Scientometrics 127 (5), 2313-2349, 2022
172022
Antonyms are similar: Towards paradigmatic association approach to rating similarity in SimLex-999 and WordSim-353
T Kliegr, O Zamazal
Data & Knowledge Engineering 115, 174-193, 2018
152018
Editable machine learning models? A rule-based framework for user studies of explainability
S Vojíř, T Kliegr
Advances in Data Analysis and Classification 14 (4), 785-799, 2020
142020
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
142014
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Artiklar 1–20