CIplus
Der Forschungsschwerpunkt CIplus ist im Cluster Computational Services and Software Quality der TH Köln angesiedelt. Ziel ist die Verbesserung des internen Austausches und der externen Sichtbarkeit der Fachdisziplinen.
Weitere Informationen zum Forschungsschwerpunkt erhalten Sie auf der Webseite Computational Intelligence plus - CIplus.
Herausgeber:
Prof. Dr. Thomas Bartz-Beielstein (Schriftenleiter)
Prof. Dr. Wolfgang Konen
Prof. Dr. Boris Naujoks
Weitere Informationen zum Forschungsschwerpunkt erhalten Sie auf der Webseite Computational Intelligence plus - CIplus.
Herausgeber:
Prof. Dr. Thomas Bartz-Beielstein (Schriftenleiter)
Prof. Dr. Wolfgang Konen
Prof. Dr. Boris Naujoks
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Document Type
- Conference Proceeding (1)
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Language
- English (2)
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Keywords
- Bayesian Regression (1)
- Data Analysis (1)
- Data Modelling (1)
- Evolutionary Robotics (1)
- Gaussian Process (1)
- Neural Networks (1)
- Parameter Tuning (1)
- Social Learning (1)
5/2018
Modelling Zero-inflated Rainfall Data through the Use of Gaussian Process and Bayesian Regression
(2018)
Rainfall is a key parameter for understanding the water cycle. An accurate rainfall measurement is vital in the development of hydrological models. By means of indirect measurement, satellites can nowadays estimate the rainfall around the world. However, these measurements are not always accurate. As a first approach to generate a bias-corrected rainfall estimate using satellite data, the performance of Gaussian process and Bayesian regression is studied. The results show Gaussian process as the better option for this dataset but leave place to improvements on both modelling strategies.
5/2017
Social learning enables multiple robots to share learned experiences while completing a task. The literature offers examples where robots trained with social learning reach a higher performance compared to their individual learning counterparts. No explanation has been advanced for that observation. In this research, we present experimental results suggesting that a lack of tuning of the parameters in social learning experiments could be the cause. In other words: the better the parameter settings are tuned, the less social learning can improve the system performance.