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Challenges of Reliable, Realistic and Comparable Active Learning Evaluation
by Daniel Kottke, Adrian Calma, Denis Huseljic, Georg Krempl and Bernhard Sick Active learning has the potential to save costs by intelligent use of resources in form of some expert’s knowledge. Nevertheless, these methods are still not established in real-world applications as they can not be evaluated properly in the specific scenario because evaluation data […]
Probabilistic Active Learning for Active Class Selection
by Daniel Kottke, Georg Krempl, Marianne Stecklina, Cornelius Styp von Rekowski, Tim Sabsch, Tuan Pham Minh, Matthias Deliano, Myra Spiliopoulou, Bernhard Sick In machine learning, active class selection (ACS) algorithms aim to actively select a class and ask the oracle to provide an instance for that class to optimize a classifier’s performance while minimizing the […]
Multi-Class Probabilistic Active Learning
by Daniel Kottke, Georg Krempl, Dominik Lang, Johannes Teschner, Myra Spiliopoulou This work addresses active learning for multi-class classification. Active learning algorithms optimize classifier performance by successively selecting the most beneficial instances from a pool of unlabeled instances to be labeled by an oracle. In this work, we study the influence of the following factors […]
Active Selection of Difficult Classes
by Marianne Stecklina, Tuan Phan Minh, Tim Sabsch, Cornelius Styp von Rekowski, Daniel Kottke, Georg Krempl, Matthias Deliano, Myra Spiliopoulou. In multi-class classification, datasets often contain both classes that can be easily separated from others and classes that require many data to learn an expressive decision boundary from. In active class selection (ACS), the main […]