Andreas Huemer

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Microarrays are being currently used for the expression levels of thousands of genes simultaneously. They present new analytical challenges because they have a very high input dimension and a very low sample size. It is highly complex to analyse multi-dimensional data with complex geometry and to identify low-dimensional “principal objects” that relate to(More)
Since machine learning has become a tool to make more efficient design of sophisticated systems, we present in this paper a novel methodology to create powerful neural network controllers for complex systems while minimising the design effort. Using a robot task as a case study, we have shown that using the feedback from the robot itself, the system can(More)
Usually, many high-skilled human resources are required to create sophisticated control systems. Automatic generation of control systems can overcome these requirements. Because of their versatility and flexibility neural networks gained an important role for this task. While evolutionary methods have been relatively successful in generating neural(More)
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