KNOWLEDGE MANAGEMENT

AND DISCOVERY LAB

2016

Benjamin Bergner, and Georg Krempl. Active Subtopic Detection in Multitopic Data. In Georg Krempl, Vincent Lemaire, Edwin Lughofer, and Daniel Kottke (Eds.), Proc. of the IKNOW-Workshop on Active Learning: Applications, Foundations and Emerging Trends, CEUR Workshop Proceedings, 2016. URL

Pawel Matuszyk, Rene Tatua Castillo, Daniel Kottke, and Myra Spiliopoulou. A Comparative Study on Hyperparameter Optimization for Recommender Systems. In Elisabeth Lex, Roman Kern, Alexander Felfernig, Kris Jack, Dominik Kowald, and Emanuel Lacic (Eds.), Workshop on Recommender Systems and Big Data Analytics (RS-BDA'16) @ iKNOW 2016, 2016. URL

Marianne Stecklina, Tuan Phan Minh, Tim Sabsch, Cornelius Styp von Rekowski, Daniel Kottke, Georg Krempl, Matthias Deliano, and Myra Spiliopoulou. Active Selection of Difficult Classes. Tagung der Deutschen Arbeitsgemeinschaft Statistik (DAGSTAT), 2016. URL

Georg Krempl, Vincent Lemaire, Edwin Lughofer, and Daniel Kottke. Tutorial on Active Learning: Applications, Foundations and Emerging Trends. 2016.

Michael Hanke, Nico Adelhöfer, Daniel Kottke, Vittorio Iacovella, Ayan Sengupta, Falko R. Kaule, Roland Nigbur, Alexander Q. Waite, Florian Baumgartner, and Jörg Stadler. A studyforrest extension, simultaneous fMRI and eye gaze recordings during prolonged natural stimulation. Scientific Data, (3)160092Nature Publishing Group, October 2016. URL

Dominik Lang, Daniel Kottke, Georg Krempl, and Myra Spiliopoulou. Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning. In Georg Krempl, Vincent Lemaire, Edwin Lughofer, and Daniel Kottke (Eds.), Active Learning: Applications, Foundations and Emerging Trends @iKnow 2016, 25-34, 2016. URL

Workshop on Active Learning: Applications, Foundations and Emerging Trends. In Georg Krempl, Vincent Lemaire, Edwin Lughofer, and Daniel Kottke (Eds.), CEUR Workshop Proceedings, (1707)Aachen University, Aachen, 2016. URL

Daniel Kottke, Georg Krempl, Dominik Lang, Johannes Teschner, and Myra Spiliopoulou. Multi-Class Probabilistic Active Learning. In Gal A. Kaminka, Maria Fox, Paolo Bouquet, Eyke Hüllermeier, Virginia Dignum, Frank Dignum, and Frank van Harmelen (Eds.), ECAI, (285):586-594, IOS Press, 2016. URL

Daniel Kottke, Georg Krempl, and Myra Spiliopoulou. Temporal Aspects of Stream Active Learning. Tagung der Deutschen Arbeitsgemeinschaft Statistik (DAGSTAT), 2016. URL

Georg Krempl, Daniel Kottke, Tuan Pham Minh, Fabian Göcke, Sebastian Hesse, Tino Reising, and Myra Spiliopoulou. Inferring Delayed Neural Network Connections. In John Aston, Claudia Kirch, and Hernando Ombao (Eds.), Conference on Novel Statistical Methods in Neuroscience (NeuroStat 2016), 2016.

Daniel Kottke, Georg Krempl, Marianne Stecklina, Cornelius Styp von Rekowski, Tim Sabsch, Tuan Pham Minh, Matthias Deliano, Myra Spiliopoulou, and Bernhard Sick. Probabilistic Active Learning for Active Class Selection. In Kory Mathewson, Kaushik Subramanian, and Robert Loftin (Eds.), Proc. of the NIPS Workshop on the Future of Interactive Learning Machines, 2016. URL

2015

Georg Krempl, Daniel Kottke, and Vincent Lemaire. Optimised probabilistic active learning (OPAL) For Fast, Non-Myopic, Cost-Sensitive Active Classification. In João Gama, Indrė Žliobaitė, Alípio M. Jorge, and Concha Bielza (Eds.), Machine Learning, 1-28, Springer US, 2015. URL

Daniel Kottke, Georg Krempl, and Myra Spiliopoulou. Probabilistic Active Learning in Datastreams. In Elisa Fromont, Tijl De Bie, and Matthijs van Leeuwen (Eds.), Advances in Intelligent Data Analysis XIV, (9385):145-157, Springer International Publishing, 2015. URL

Georg Krempl, Daniel Kottke, and Vincent Lemaire. Optimised probabilistic active learning (OPAL). In João Gama, Indrė Žliobaitė, Alípio M. Jorge, and Concha Bielza (Eds.), Machine Learning, 1-28, Springer US, 2015. URL

Daniel Kottke, Gino Gulamhussene, and Klaus Tönnies. Data-Driven Spine Detection for Multi-Sequence MRI. In Heinz Handels, Thomas Martin Deserno, Hans-Peter Meinzer, and Thomas Tolxdorff (Eds.), Bildverarbeitung für die Medizin (BVM2015), 5-10, Springer Berlin Heidelberg, 2015. URL

2014

Georg Krempl, Daniel Kottke, and Myra Spiliopoulou. Probabilistic Active Learning: A Short Proposition. In Torsten Schaub, Gerhard Friedrich, and Barry O'Sullivan (Eds.), Proceedings of the 21st European Conference on Artificial Intelligence (ECAI2014), August 18 -- 22, 2014, Prague, Czech Republic, (263)IOS Press, 2014. URL

Georg Krempl, Daniel Kottke, and Myra Spiliopoulou. Probabilistic Active Learning: Towards Combining Versatility, Optimality and Efficiency. In Saso Dzeroski, Pance Panov, Dragi Kocev, and Ljupco Todorovski (Eds.), Proceedings of the 17th Int. Conf. on Discovery Science (DS), Bled, Springer, 2014. URL