Tobias Girschick

TL;DR: Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way.

TL;DR: This paper presents a novel method for structural graph clustering without generating features or decomposing graphs into parts and takes advantage of the frequent subgraph miner gSpan without effectively generating thousands of subgraphs in the process.

TL;DR: The results show that in many cases the application of local models significantly improves the predictive power of the derived (Q)SAR models compared to the classical approach, to models that are induced by a fingerprint‐based or a hierarchical clustering approach and to locally weighted learning.

TL;DR: A novel method for finding suitable combinations of distance measures, called adapted transfer, which adapts a distance measure learned on another, related dataset to a given dataset is presented, which combines distance learning and transfer learning in a novel manner.

TL;DR: Experiments show that a kernel estimator based on class probability estimates from a random forest classifier is highly competitive with Gaussian process regression, while taking only a fraction of the time for training.