61 citations · 224 across the 33 of their papers we have counts for
5 papers · 2 filters
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…
Emerging Relation Network and Task Embedding for Multi-Task Regression Problems
Jens Schreiber, Bernhard Sick
Multi-task learning (mtl) provides state-of-the-art results in many applications of computer vision and natural language processing. In contrast to single-task learning (stl), mtl…
Extended Coopetitive Soft Gating Ensemble
Stephan Deist, Jens Schreiber, Maarten Bieshaar +1
This article is about an extension of a recent ensemble method called Coopetitive Soft Gating Ensemble (CSGE) and its application on power forecasting as well as motion primitive f…
Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning
Christian Haase-Schütz, Rainer Stal, Heinz Hertlein +1
State-of-the-art, high capacity deep neural networks not only require large amounts of labelled training data, they are also highly susceptible to label errors in this data, typica…