Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen


Geiger Bernhard, Schrunner Stefan, Kern Roman

An Information-Theoretic Measure for Pattern Similarity in Analog Wafermap

European Advanced Process Control and Manufacturing Conf. (apc|m, Villach, 2019

Schrunner and Geiger have contributed equally to this work.

Maritsch Martin, Diana Suleimenova, Geiger Bernhard, Derek Groen

AI-Support for large-scale Refugee Movement Simulations

Computing Systems Week Spring 2019, HiPEAC, Edinburgh, 2019


Geiger Bernhard

On the Information Dimension of Random Variables and Stochastic Processe

Workshop on Casualty and Dynamics in Brain Networks @ Int. Joint Conf. on Neural Networks, Budapest, 2019

joint work with Tobias Koch, Universidad Carlos III de Madrid

Toller Maximilian, Geiger Bernhard, Kern Roman

A Formally Robust Time Series Distance Metric

Mile'TS @ SIGKDD, Anchorage, Alaska USA, 2019

Distance-based classification is among the most competitive classification methods for time series data. The most critical componentof distance-based classification is the selected distance function.Past research has proposed various different distance metrics ormeasures dedicated to particular aspects of real-world time seriesdata, yet there is an important aspect that has not been considered so far: Robustness against arbitrary data contamination. In thiswork, we propose a novel distance metric that is robust against arbitrarily “bad” contamination and has a worst-case computationalcomplexity of O(n logn). We formally argue why our proposedmetric is robust, and demonstrate in an empirical evaluation thatthe metric yields competitive classification accuracy when appliedin k-Nearest Neighbor time series classification.

Schweimer Christoph, Geiger Bernhard, Suleimenova Diana, Groen Derek, Gfrerer Christine, Pape David, Elsaesser Robert, Kocsis Albert Tihamér, Liszkai B., Horváth Zoltan

Model Reduction in HiDALGO - Initial Plans and Ideas

Workshop on Model Reduction of Complex Dynamical Systems (MODRED), Graz, 2019

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