Edgar Kalkowski
Edgar Kalkowski
Ph.D. student, Intelligent Embedded Systems Lab, University of Kassel, Germany
Verifisert e-postadresse på uni-kassel.de - Startside
TittelSitert avÅr
Learning from others: Exchange of classification rules in intelligent distributed systems
D Fisch, M Jänicke, E Kalkowski, B Sick
Artificial Intelligence 187, 90-114, 2012
Knowledge fusion for probabilistic generative classifiers with data mining applications
D Fisch, E Kalkowski, B Sick
IEEE Transactions on Knowledge and Data Engineering 26 (3), 652-666, 2013
Techniques for knowledge acquisition in dynamically changing environments
D Fisch, M Jänicke, E Kalkowski, B Sick
ACM Transactions on Autonomous and Adaptive Systems (TAAS) 7 (1), 16, 2012
In your interest-objective interestingness measures for a generative classifier
D Fisch, E Kalkowski, B Sick, SJ Ovaska
International Conference on Agents and Artificial Intelligence 2, 414-423, 2011
Collaborative learning by knowledge exchange
D Fisch, E Kalkowski, B Sick
Organic Computing—A Paradigm Shift for Complex Systems, 267-280, 2011
Learning by teaching versus learning by doing: Knowledge exchange in organic agent systems
D Fisch, M Janicke, E Kalkowski, B Sick
2009 IEEE Symposium on Intelligent Agents, 31-38, 2009
Towards automation of knowledge understanding: An approach for probabilistic generative classifiers
D Fisch, C Gruhl, E Kalkowski, B Sick, SJ Ovaska
Information Sciences 370, 476-496, 2016
Using ontology-based similarity measures to find training data for problems with sparse data
E Kalkowski, B Sick
2015 IEEE International Conference on Systems, Man, and Cybernetics, 1693-1699, 2015
Generative Vorhersagetechniken für Raten und Ontologie-basierte Ähnlichkeitsberechnung mit Anwendungen im Suchmaschinenmarketing
E Kalkowski
kassel university press GmbH, 2019
Correlation of Ontology-Based Semantic Similarity and Human Judgement for a Domain Specific Fashion Ontology
E Kalkowski, B Sick
International Conference on Web Engineering, 207-224, 2016
Generative Exponential Smoothing and Generative ARMA Models to Forecast Time-Variant Rates or Probabilities
E Kalkowski, B Sick
Time Series Analysis and Forecasting, 75-88, 2016
Self-Extending Training Sets: Using Ontologies to Improve Machine Learning Performance
E Kalkowski
Organic Computing: Doctoral Dissertation Colloquium 2014 4, 111, 2014
D Fisch, E Kalkowski, B Sick, SJ Ovaska
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Artikler 1–13