8 citations · 14 across the 4 of their papers we have counts for
4 papers · 1 filter
Probabilistic Active Learning for Active Class Selection
Daniel Kottke, Georg Krempl, Marianne Stecklina +6
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 perf…
Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary
Adrian Englhardt, Holger Trittenbach, Daniel Kottke +2
Support Vector Data Description (SVDD) is a popular one-class classifiers for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size.…
Toward Optimal Probabilistic Active Learning Using a Bayesian Approach
Daniel Kottke, Marek Herde, Christoph Sandrock +3
Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs…
Limitations of Assessing Active Learning Performance at Runtime
Daniel Kottke, Jim Schellinger, Denis Huseljic +1
Classification algorithms aim to predict an unknown label (e.g., a quality class) for a new instance (e.g., a product). Therefore, training samples (instances and labels) are used…