7 citations · 11 across the 6 of their papers we have counts for
6 papers
ConstraintMatch for Semi-constrained Clustering
Jann Goschenhofer, Bernd Bischl, Zsolt Kira
Constrained clustering allows the training of classification models using pairwise constraints only, which are weak and relatively easy to mine, while still yielding full-supervisi…
Approximately Bayes-Optimal Pseudo Label Selection
Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti +2
Semi-supervised learning by self-training heavily relies on pseudo-label selection (PLS). The selection often depends on the initial model fit on labeled data. Early overfitting mi…
Uncertainty-aware Pseudo-label Selection for Positive-Unlabeled Learning
Emilio Dorigatti, Jann Goschenhofer, Benjamin Schubert +2
Positive-unlabeled learning (PUL) aims at learning a binary classifier from only positive and unlabeled training data. Even though real-world applications often involve imbalanced…
Deep Semi-Supervised Learning for Time Series Classification
Jann Goschenhofer, Rasmus Hvingelby, David Rügamer +3
While Semi-supervised learning has gained much attention in computer vision on image data, yet limited research exists on its applicability in the time series domain. In this work,…
Granular Motor State Monitoring of Free Living Parkinson's Disease Patients via Deep Learning
Kamer A. Yuksel, Jann Goschenhofer, Hridya V. Varma +2
Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide and affects around 1% of the (60+ years old) elderly population in industrial nations. More t…
Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning
Jann Goschenhofer, Franz MJ Pfister, Kamer Ali Yuksel +3
One major challenge in the medication of Parkinson's disease is that the severity of the disease, reflected in the patients' motor state, cannot be measured using accessible biomar…