Abstract
The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.
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Foresti, L., Tuia, D., Pozdnoukhov, A., Kanevski, M. (2009). Multiple Kernel Learning of Environmental Data. Case Study: Analysis and Mapping of Wind Fields. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_94
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DOI: https://doi.org/10.1007/978-3-642-04277-5_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04276-8
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