We perform teaching and research in machine learning strategies for the pattern analysis of various kinds of data. This comprises statistical models for clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data.
We perform teaching and research in machine learning strategies for the pattern analysis of various kinds of data. This comprises statistical models for clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data.
Publications - page 8
Boosting convolutional filters with entropy sampling for optic cup and disc image segmentation from fundus images
Automatic single cell segmentation on highly multiplexed tissue images
Asymptotic analysis of estimators on multi-label data
Multi-label classification extends the standard multi-class classification paradigm by dropping the assumption that classes have to be mutually exclusive, i.e., the same data item might belong to more than one class. Multi-label classification has many important applications in e.g. signal processing, medicine, biology and information security, but the analysis and understanding of the inference methods based on data with multiple labels are still underdeveloped. In this paper, we formulate a general generative process for multi-label data, i.e. we associate each...