23.5.2018 · Torsten Möller (Universität Wien) · Understanding multi-dimensional spaces


 When we build models, in Data Science or in Computational Science, we need to specify an objective function to find (or learn) a best model. However, in many scenarios there are multiple objectives one has to consider. Often times, we simply specify a weighted average among relevant objective functions. In this talk I will report on how to deal with cases where such a weighting is not clear or possible: how can we deal with multiple objectives in model building? Creating a visual analysis workflow that complements algorithmic and mathematical analysis workflows I will try to convince you that we are able to better understand multi-dimensional problems and solution spaces.