Kottke, Daniel

2016

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

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

Active Selection of Difficult Classes. Tagung der Deutschen Arbeitsgemeinschaft Statistik (DAGSTAT), 2016. URL

Tutorial on Active Learning: Applications, Foundations and Emerging Trends. 2016.

A studyforrest extension, simultaneous fMRI and eye gaze recordings during prolonged natural stimulation. Scientific Data, (3)160092Nature Publishing Group, October 2016. URL

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

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

Temporal Aspects of Stream Active Learning. Tagung der Deutschen Arbeitsgemeinschaft Statistik (DAGSTAT), 2016. URL

Inferring Delayed Neural Network Connections. In John Aston, Claudia Kirch, and Hernando Ombao (Eds.), Conference on Novel Statistical Methods in Neuroscience (NeuroStat 2016), 2016.

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

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

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

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

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

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

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

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