Dörthe Malzahn

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Based on a statistical mechanics approach, we develop a method for approximately computing average case learning curves for Gaussian process regression models. The approximation works well in the large sample size limit and for arbitrary dimensionality of the input space. We explain how the approximation can be systematically improved and argue that similar(More)
Processes: A Statistical Mechanics Study Dörthe Malzahn, Manfred Opper (1) Informatics and Mathematical Modelling, Technical University of Denmark, Richard-Petersens-Plads Building 321, DK-2800 Lyngby, Denmark (2) School of Engineering and Applied Science / NCRG, Aston University, Birmingham B4 7ET, United Kingdom (Dated: May 27, 2002) Abstract We employ(More)
Using a novel reformulation, we develop a framework to compute approximate resampling data averages analytically. The method avoids multiple retraining of statistical models on the samples. Our approach uses a combination of the replica “trick” of statistical physics and the TAP approach for approximate Bayesian inference. We demonstrate our approach on(More)
Studies in 15 industries revealed characteristic empirical relationships between workplace environmental conditions and outside weather conditions. These relationships, expressed in the form of predictive models for Wet Bulb Globe Temperature, can be used to estimate WBGT from weather forecasts, weather reports, or current meterorological measurements.
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