Abstract The tensor kernel has been used across the machine learning literature for a number of purposes and applications, due to its ability to incorporate samples from multiple s...
If appropriately used, prior knowledge can significantly improve the predictive accuracy of learning algorithms or reduce the amount of training data needed. In this paper we intr...
Supervised clustering is the problem of training a clustering algorithm to produce desirable clusterings: given sets of items and complete clusterings over these sets, we learn ho...
Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM ...
A particularly difficult task in molecular imaging is the analysis of fluorescence microscopy images of neural tissue, as they usually exhibit a high density of objects with diffu...
Julia Herold, Manuela Friedenberger, Marcus Bode, ...