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Ort:DFKI Bremen
Robotics Innovation Center
Robert-Hooke-Str. 5
Konferenzraum 117
Opens internal link in current windowAnfahrtsbeschreibung
Jesse Read,  University of Waikato
Multi-label Classification
(Abstract)
Multi-label classification is the supervised classification context in machine learning where each data point may belong to multiple labels, as opposed to a single class label like in the traditional multi-class problem. Multi-label classification is natural to many domains, such as computer vision, text classification, robotics, and bioinformatics.

One of the main issues involved in multi-label classification is the importance of detecting and incorporating correlations between labels into the learning process. A second and related issue is the additional complexity involved in multi-label learning, as compared to the single-label context.

This talk will focus on some of the applications of, and common approaches to multi-label learning, with an emphasis on scalability to large datasets and data streams.