| Warburger Str. 100, room O2
Title of the talk: "From unstructured to structured target spaces in multi-target prediction"
Traditional methods in machine learning and statistics provide data-driven models for predicting one-dimensional targets, such as binary outputs in classification and real-valued outputs in regression. In recent years, novel application domains have triggered fundamental research on more complicated problems where multi-target predictions are required. Such problems arise in diverse application domains, such as document categorization, tag recommendation of images, videos and music, information retrieval, medical decision making, drug discovery, marketing, biology, geographical information systems, etc.
In this talk I will elaborate on the differences between unstructured and structured output spaces in multi-target prediction. When the output space is structured, one can use this information to generalize towards targets that have never been observed during the training phase. This is the so-called zero-shot learning setting. Several approaches for tackling problems of that kind will be discussed.