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We thank the team of the LiDO HPC cluster at the TU Dortmund for their technical support while developing and using both R packages. We also thank Heike Trautmann, Oliver Flasch and Uwe Ligges for helpful discussions and comments. Abstract Empirical analysis of statistical algorithms often demands time-consuming experiments which are best performed on high(More)
Carrying out a statistical analysis, the researcher is concerned with the problem of choosing an appropriate statistical technique from a large number of competing methods. Most common statistical software offer different methods for analysing the data without giving any support regarding the adequacy of a method for a particular data set. This paper(More)
The paper describes a case study in combining diierent methods for acquiring medical knowledge. Given a huge amount of noisy, high dimensional numerical time series data describing patients in intensive care, the support vector machine is used to learn when and how to change the dose of which drug. Given medical knowledge about and expertise in clinical(More)
The recognition of objects and, hence, their descriptions must be grounded in the environment in terms of sensor data. We argue, why the concepts, used to classify perceived objects and used to perform actions on these objects, should integrate action-oriented perceptual features and perception-oriented action features. We present a grounded symbolic(More)
Large media collections rapidly evolve in the World Wide Web. In addition to the targeted retrieval as is performed by search engines, browsing and explorative navigation is an important issue. Since the collections grow fast and authors most often do not annotate their web pages according to a given ontology, automatic structuring is in demand as a(More)
The application of machine learning techniques in compiler frameworks has become a challenging research area. Learning algorithms are exploited for an automatic generation of optimization heuristics which often outperform hand-crafted models. Moreover, these automatic approaches can effectively tune the compilers' heuristics after larger changes in the(More)
1 The need for sustainability According to Brundtland Commission of the United Nations, sustainability can be defined as " capacity to endure the needs of today's population without jeopardizing the ability of the future generations to meet their own needs ". Sustainability implies resource consumption with little internal or external adverse impact. A(More)
When learning from very large databases, the reduction of complexity is of highest importance. Two extremes of making knowledge discovery in databases (KDD) feasible have been put forward. One extreme is to choose a most simple hypothesis language and so to be capable of very fast learning on real-world databases. The opposite extreme is to select a small(More)
Modern sensing technology allows us enhanced monitoring of dynamic activities in business, traffic, and home, just to name a few. The increasing amount of sensor measurements, however, brings us the challenge for efficient data analysis. This is especially true when sensing targets can interoperate—in such cases we need learning models that can capture the(More)